Massive number of simultaneous Internet of Things (IoT) uplinks strain gateways with interference and energy limits, yet devices often lack neighbors' Channel State Information (CSI) and cannot sustain centralized Mobile Edge Computing (MEC) or heavy Machine Learning (ML) coordination. Classical Bayesian solvers help with uncertainty but become intractable as users and strategies grow, making lightweight, distributed control essential. In this paper, we introduce the first-ever, novel epistemic Bayesian game for uplink power control under incomplete CSI that operates while suppressing interference among multiple uplink channels from distributed IoT devices firing at the same time. Nodes run inter-/intra-epistemic belief updates over opponents' strategies, replacing exhaustive expected-utility tables with conditional belief hierarchies. Using an exponential-Gamma SINR model and higher-order utility moments (variance, skewness, kurtosis), the scheme remains computationally lean with a single-round upper bound of $O\!\left(N^{2} S^{2N}\right)$. Precise power control and stronger coverage amid realistic interference: with channel magnitude equal to $1$ and a signal-to-interference-plus-noise ratio (SINR) threshold of $-18$ dB, coverage reaches approximately $60\%$ at approximately $55\%$ of the maximum transmit power; mid-rate devices with a threshold of $-27$ dB achieve full coverage with less than $0.1\%$ of the maximum transmit power.Under $80\%$ interference, a fourth-moment policy cuts average power from approximately $52\%$ to approximately $20\%$ of the maximum transmit power with comparable outage, outperforming expectation-only baselines. These results highlight a principled, computationally lean path to optimal power allocation and higher network coverage under real-world uncertainty within dense, distributed IoT networks.

Precise clock synchronization underpins deterministic operation in wireless systems spanning industrial automation, vehicular networks, distributed extended reality (XR), smart infrastructure, and wide-area precision agriculture. Wireless links introduce variable propagation delays, channel asymmetry, interference, clock drift, and scalability constraints that make sub-microsecond alignment difficult. This article provides a comprehensive survey and tutorial on wireless clock synchronization. We introduce a five-dimension taxonomy covering: system architecture, synchronization mechanism, correction strategy, delay and uncertainty modeling, and resource and deployment class, and apply it to eight canonical protocol families and to synchronization as realized across IEEE 802.15.4, ZigBee, Bluetooth low energy (BLE), LoRa, Wi-Fi, Ultrawide band (UWB), and 4G/5G/6G systems. We examine solutions across five application domains: industrial automation and Industrial Internet of Things (IIoT), vehicular V2X, distributed XR and metaverse, infrastructure monitoring, and wide-area Internet of Things (IoT) and precision agriculture; alongside tools, testbeds, and datasets supporting evaluation. Open challenges addressed include scalability and mobility, ultralow-jitter determinism, robust clock parameter estimation under non-Gaussian delay distributions, distributed and consensus-based synchronization for infrastructure-free networks, secure and resilient synchronization against wireless-specific threats, and cross-domain convergence encompassing time-sensitive networking (TSN)–5G/6G interoperability and joint communication, sensing, and timing as an emerging 6G design paradigm. Together, these contributions provide the first unified cross-technology framework connecting fundamentals, protocol families, application domains, and open research challenges in wireless clock synchronization.

Time-Sensitive Networking (TSN) is increasingly adopted in industrial systems to meet strict latency, jitter, and reliability requirements. However, evaluating TSN’s fault tolerance under realistic failure conditions remains challenging. This paper presents IN2C, a modular OMNeT++/INET-based simulation framework that models two synchronized production cells connected to centralized infrastructure. IN2C integrates core TSN features—including time synchronization, traffic shaping, per-stream filtering, and Frame Replication and Elimination for Redundancy (FRER)—alongside XML-driven fault injection for link and node failures. Four fault scenarios are evaluated to compare TSN performance with and without redundancy. Results show that FRER eliminates packet loss and achieves sub-millisecond recovery, though with 2–3× higher link utilization. These findings offer practical guidance for deploying TSN in bandwidth-constrained industrial environments.

Private 5G networks are emerging as key enablers for smart factories, where a single device often handles multiple concurrent traffic flows with distinct Quality of Service (QoS) requirements. Existing simulation frameworks, however, lack the fidelity to model such multi-flow behavior at the QoS Flow Identifier (QFI) level. This paper addresses this gap by extending Simu5G to support per-QFI modeling and by introducing a novel QoS-aware Proportional Fairness (QoS-PF) scheduler. The scheduler dynamically balances delay, Guaranteed Bit Rate (GBR), and priority metrics to optimize resource allocation across heterogeneous flows. We evaluate the proposed approach in a realistic smart factory scenario featuring edge-hosted machine vision, real-time control loops, and bulk data transfer. Results show that QoS-PF improves deadline adherence and fairness without compromising throughput. All extensions are implemented in a modular and open-source manner to support future research. Our work provides both a methodological and architectural foundation for simulating and analyzing advanced QoS policies in industrial 5G deployments.

Short-term load forecasting (STLF) is essential for the efficient operation and stability of modern power systems, particularly in smart grids with high penetration of renewable energy sources. In this study, we propose an enhanced Attentive Neural Process (ANP) framework for STLF, incorporating a customized loss function with an additional term to improve predictive performance. The ANP framework enables personalized and accurate forecasting by leveraging its latent variable representation and attention mechanism to adapt predictions based on newly observed consumption patterns. Its ability to encode an arbitrary number of observations allows for efficient real-time updates without requiring extensive retraining, allowing for rapid and continuous adaptation to evolving data. Through probabilistic modeling, ANP can quantify uncertainty in predictions, which can play a crucial role in risk-aware decision-making in power systems. Experimental results show that our ANP based approach achieves the highest prediction accuracy, reducing the Mean Average Error (MAE) by between 14.95% and 60.97% compared to a range of alternative learning approaches.

The Collective Perception Service (CPS) enables vehicles and infrastructure to cooperatively share local sensor measurements to extend their perception range for a more comprehensive understanding of their environment. Although CPS transmissions are useful, their frequency and redundancy can saturate the vehicular communication channel hindering transmissions of other critical messages. To mitigate this, ETSI has specified generation rules for invoking Collective Perception Messages (CPMs), which are mainly based on objects’ dynamics and frequency of reporting but do not account for the perceptual accuracy of measurements. This can lead to ETSI generation rules potentially being invoked more frequently for inaccurately sensed objects, unnecessarily consuming network resources. Furthermore, inaccurate sensor data is more likely to be matched to the incorrect object track, yielding information that can mislead decision-making. The CAR-2-CAR Communication Consortium (C2C-CC) has proposed an object quality pre-filter to remove poor-quality measurements before triggering the collective perception generation rules, but its complexity has hindered deployment. At the request of ETSI and to hasten deployment, this paper explores the feasibility of proposing a ’simple’ pre-filter that will attempt to discard measurements that would be falsely associated based on a position error measure-based threshold. We find that implementing such a filter is complex and highly dependent on the environmental topology, road user type, geo-spatial density and temporal factors. Specifically, we investigate false associations under different sensor error ranges for diverse road users across 4 urban intersections, detailing their causation. We discuss the challenges in setting a distance measure-based threshold and evaluate its implications as a pre-filter to reduce the number of false and weakly associated objects in a CPM.

Time-Sensitive Networking (TSN) standards have enabled deterministic Ethernet solutions, providing low-latency, reliable communication critical for Industry 4.0 applications. With industrial environments increasingly incorporating wireless solutions, the integration of TSN over 5G and Wi-Fi has emerged as a key research focus. This paper provides a comparative analysis of the integration of 5 G and Wi-Fi with TSN, evaluating latency, reliability, scalability, synchronization, mobility support, and implementation complexity. The simulation results identify the conditions under which 5G+TSN or Wi-Fi+TSN offers superior performance, offering clear recommendations for the selection of technology adapted to industrial use cases.

While Time-Sensitive Networking (TSN) enhances the determinism, real-time capabilities, and reliability of Eth-ernet, future industrial networks will not only use wired but increasingly wireless communications. Wireless networks enable mobility, have lower costs, and are easier to deploy. However, for many industrial applications, wired connections remain the preferred choice, particularly those requiring strict latency bounds and ultra-reliable data flows, such as for controlling machinery or managing power electronics. The emergence of SG, with its Ultra-Reliable Low-Latency Communication (URLLC) promises to enable high data rates, ultra-low latency, and minimal jitter, presenting a new opportunity for wireless in-dustrial networks. However, as 5G networks include wired links from the base station towards the core network, a combination of 5G with time-sensitive networking is needed to guarantee stringent QoS requirements. In this paper, we evaluate SG-TSN performance for different indoor factory applications and environments through simulations. Our findings demonstrate that 5G- TSN can address latency-sensitive scenarios in indoor factory environments.

While technologies such as Time-Sensitive Networking (TSN) improve deterministic behaviour, real-time functionality, and robustness of Ethernet, future industrial networks aim to be increasingly wireless. While wireless networks facilitate mobility, reduce cost, and simplify deployment, they do not always provide stringent latency constraints and highly dependable data transmission as required by many manufacturing systems. The advent of 5G, with its Ultra-Reliable Low-Latency Communication (URLLC) capabilities, offers potential for wireless industrial networks. 5G offers elevated data throughput, very low latency, and negligible jitter. As 5G networks typically include wired connections from the base station to the core network, integration of 5G with time-sensitive networking is essential to provide rigorous QoS standards. This paper assesses the scalability of 5G-TSN for various indoor factory applications and conditions using OMNET++ simulation. Our research shows that 5G-TSN has the potential to provide bounded delay for latency-sensitive applications in scalable indoor factory settings.

Efficient and reliable resource allocation within densely-deployed massive IoT networks remains a key challenge due to resource constraints among low size, weight and power (SWaP) IoT devices and within the network and limitations of conventional centralized methods under incomplete information. We propose a novel auction-based framework for adaptive resource allocation, combining space-time-frequency spreading (STFS) techniques with Bayesian Game approaches. We introduce novel modified Simultaneous Ascending Auction (mSAA) mechanism tailored to densely-deployed and low-complexity IoT networks, enabling distributed computation and reduced power consumption. By incorporating Bayesian game-based bidding strategies and optimizing dispersion matrices for signal transmission, the proposed approach ensures enhanced channel throughput and energy efficiency. Comparative analysis against traditional auction types, including First-Price and Second-Price Sealed-Bid Auctions, as well as the Vickrey–Clarke–Groves (VCG) mechanism, demonstrates the superiority of mSAA in terms of surplus maximization, revenue efficiency, and robustness in risk-prone bidding environments. Simulation results validate the model’s adaptability to heterogeneous IoT nodes and its potential for dense deployment across different environments and verticals.

The Social Internet of Vehicles (SIoV) is a concept combining the principles of vehicular and social networks, where entities, such as vehicles, drivers, passengers and infrastructure, share information not only for intelligent transportation or cooperative mobility needs, but also using social network principles. Trust in the information exchanged between vehicles in a vehicular network is paramount to achieving safety and reliability of transportation. We propose a trust management model for SIoV that integrates entity trust from direct interactions between vehicles, indirect trust from recommendations, and social trust reflecting the drivers’ social attributes. We utilize Dempster–Shafer Theory to effectively manage inherent uncertainties within this network, enabling robust aggregation of various trust evidences. Our simulation results show the effectiveness of our model in accurately identifying and mitigating malicious entities within the network performing trust-related attacks.

The tenth IEEE International Conference on Smart Computing (SmartComp 2024), sponsored by the IEEE Computer Society, will be held in-person in Osaka, Japan. It continues the tradition of the previous editions in presenting high-quality research and technology in smart and connected computing.

Differentially Private Federated Learning (DP-FL) is a privacy-preserving machine learning paradigm. Building on a standard DP-FL approach, we introduce and implement a novel Differentially Private Multi-Tier Federated Learning approach specifically tailored for IoT applications, specifically short-term load forecasting. Our method integrates a Sampled Gaussian Mechanism for differential privacy with a hierarchical federated learning approach, where local federations participate in learning while adhering to approximate differential privacy with respect to a global server. We specifically study the optimal number of local rounds on global model convergence. Our findings demonstrate that non-DP models with fewer local rounds exhibit slightly superior performance compared to DP-enabled models. However, integrating DP by introducing additional noise during training with larger local rounds enhances the generalization of global models, suggesting that the sampled Gaussian mechanism functions as a form of regularization. In the evaluation of our method, we utilise an energy consumption dataset from the UK Power Networks Low Carbon London project. Our results show that our approach achieves privacy preserving objectives while obtaining the optimal number of local rounds that minimise the prediction error.

Pressure ulcers, also known as pressure sores or bedsores, are a disease affecting the skin and underlying tissue that mostly occurs over bony parts of the body because of long-term exposure to pressure. Pressure ulcers most commonly develop in patients who are often bed-or chair-bound for long periods of time. When an ulcer occurs, its treatment is often complex and expensive. Relieving the pressure imposed on the patient skin is the most effective approach for preventing bedsores. Common practice in hospitals and nursing homes is to periodically repositioning the patient. Manually relieving patient skin pressure places a financial and physical burden on caregivers. In this paper, we are proposing a detection approach that optimally detects patient posture and spots at-risk locations on the body by using data from a pressure sensor array in a bed. Our novel ulcer detection solution is based on posture detection using an SVM-based classifier, whose performance was considerably boosted by the pre-processing pipeline we designed. The approach periodically generates outputs that can be used to trigger actuators in air-enabled mattresses to inflate and deflate the air in the target locations. This technique can also be used in robot-equipped beds to turn patients if needed to prevent bedsores. The generated outputs can also be used in reporting the patient's condition.

Many Industrial Internet of Things (IIoT) applications have zero fault tolerance. Redundant networks are hence an important prerequisite for a dependable infrastructure to serve the various data traffic classes required for such applications. In this paper, we perform a delay analysis of a Quality Checks After Production (QCAP) application, which is running on a Time-Sensitive Network (TSN). We evaluate the worst-case delay of all data flows traversing the redundant TSN network using a Network Calculus (NC) based framework. We also evaluate how network parameters and configurations affect the QCAP application's service requirements.

The combination of Composite Base Stations (CBSs) and Dynamic Spectrum Scheduling (DSS) can enable high-speed connectivity in urban environments, providing seamless connectivity to users and supporting a wide range of high-bandwidth applications. This paper investigates the performance of CBSs combining Fifth Generation (5G) cellular network and Fourth Generation (4G) Long-Term Evolution (LTE) modes in an urban environment such as a university campus. The comparison considers sub-6GHz and mmWave frequencies (28GHz) and a DSS to improve network performance and increase overall system capacity. The results show that the CBS approach improves network performance, increasing system capacity and provides high-speed connectivity to users. Additionally, this research highlights the potential of mmWave frequencies for use in heterogeneous networks to improve coverage over a large area. The findings of this study offer valuable insights for network planners and operators, demonstrating the benefits and challenges of CBS deployment

Vehicle-to-Vehicle (V2V) communication requires highly reliable wireless links to deliver safety messages effectively. Signal interference or blockages caused by big vehicles like buses and trucks can deteriorate communication quality, causing loss of packets containing safety information transmitted among vehicles. Hence big vehicle shadowing critically influences V2V communication quality. This paper investigates the impact of big vehicle shadowing on V2V communications, considering cellular network coverage of vehicles. Under big vehicle shadowing, we analyse and derive the reuse distance and outage probability of V2V communications. To mitigate the negative of big vehicle shadowing, beamforming-based reception and transmission along with relay approaches are shown. Simulation results show that the proposed approaches significantly improve the performance of V2V communications in the presence of big vehicles.

The present study proposes a novel multi-user transmission scheme, which employs the utilization of space-time block coding (STBC) in conjunction with orthogonal frequency division multiplexing (OFDM) as the transmission framework. The scheme, referred to as multi-user STBC super-positioned transmission (MU-STBC-ST), utilizes the superposition of user data to serve multiple users with spectral, temporal, and frequency resources similar to non-orthogonal multiplexing. Furthermore, the proposed scheme is deployed in conjunction with multiple-input multiple-output (MIMO) transmission to serve two users, resulting in an improved version of STBC-MIMO. To achieve this, we employ the utilization of uniquely designed auxiliary signals that are superimposed on top of already super-positioned user data during the two transmission slots, with the objective of intelligently canceling the inter-user interference and channel effects at the receiver, while simultaneously making the reception process much simpler, energy-efficient and less power-draining. Additionally, we also propose a simple equalization policy to recover the signals at the receiver while reducing complexity significantly, resulting in low latency and less processing at the receiver. The proposed scheme’s performance is evaluated through computer simulation utilizing performance metrics such as bit error rate (BER), throughput error rate (TER), and peak-to-average power ratio (PAPR) and compared with that of conventional STBC.

We are investigating the characteristics of wireless channels in a factory floor environment through simulation and experimentation. We simulate the communication coverage of 5G indoor factory (InF) networks using both, ray tracing and statistical channel models. A ray tracing model requires detailed models of the environment, including their properties and locations, whereas an empirical statistical model does not need such details. Through simulation, we compare the InF wireless channel characteristics obtained from ray tracing and statistical models. Different channel characterization metrics are considered in the comparison, including Reference Signal Received Power (RSRP), channel gain, and SNR. For the purpose of validating the simulation results, we have conducted experiments with a private 5G test-bed on a real-world factory floor. For our use case, we used a factory hall with two 5G radio heads in different positions. Our study demonstrates the viability of the 3GPP InF model, providing more accurate predictions than ray tracing while using less data and significantly quicker simulation run times.

Abstract The increasing complexity delivered by the heterogeneity of the cyber-physical systems is being addressed and decoded by edge technologies, IoT development, robotics, digital twin engineering, and AI. Nevertheless, tackling the orchestration of these complex ecosystems has become a challenging problem. Specially the inherent entanglement of the different emerging technologies makes it hard to maintain and scale such ecosystems. In this context, the usage of model-driven engineering as a more abstract form of glue-code, replacing the boilerplate fashion, has improved the software development lifecycle, democratising the access to and use of the aforementioned technologies. In this paper, we present a practical use case in the context of Smart Manufacturing, where we use several platforms as providers of a high-level abstraction layer, as well as security measures, allowing a more efficient system construction and interoperability.

5G New Radio (NR) is the latest radio access technology (RAT) developed by 3GPP for the 5G mobile network. 5G NR and beyond is expected to play a key role in Cyber-Physical Systems as it will deliver significantly faster, more reliable and much lower latency connections to enable wireless control applications. 5G will support three fundamental application scenarios, enhanced Mobile BroadBand (eMBB), Ultra-Reliable and Low deployment Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). mMTC is of particular importance as it forms the basis of IoT, whereas URLLC will support mission-critical applications such as autonomous robotics. The commercial roll-out of 5G is planned in phases with challenging new vertical deployments as the technology is still evolving and little practical experience is available yet. Massive MIMO is a vital enabling technology for 5G NR, enhancing reliability and data rates in challenging environments. It is one of the technologies having a low carbon emission rate as it exploits the resources in an optimal way, hence enabling more sustainable and greener networks. In this paper, we investigate the performance of two MIMO precoding techniques in terms of achievable sum rates for massive MIMO. Simulation experiments show that Zero Forcing (ZF) precoding outperforms Maximum Ratio Transmission (MRT) precoding for the given scenario and assumed conditions.

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

Automation systems involve a range of cyber-physical system components such as sensors, actuators, control equipment, machines, robots, AGVs, etc. Seamless interoperability among these entities is a significant challenge. A well-designed Industrial Internet of Things (IIoT) platform at the network edge can offer several services by acting as a transformation engine between these field devices and various enterprise applications. We consider the EdgeX Foundry platform as such an IIoT middleware, discuss how EdgeX can provide ready-to-use integration of IoT devices, and show how we connect it with a low-code XMDD coordination layer that interfaces with EdgeX microservices through a Native DSL mechanism. We consider this technology landscape from the point of view of a building automation system example that supports high reconfigurability and security. We show how to produce all the essential elements of a complex Web based application to control the considered building systems. We demonstrate various features of the application's data and process models, how DSLs play a role at various levels, and how to add security capabilities that go beyond the cross-layer concerns and mechanisms offered by EdgeX. To this end, we introduce a declarative policy layer to be implemented using the open source ADD-Lib in form of an additional DSL for Attribute Based Encryption, with the aim of further enriching the capabilities around the EdgeX platform.

Abstract In many industries, traditional automation systems (operating technology) such as PLCs are being replaced with modern, networked ICT-based systems as part of a drive towards the Industrial Internet of Things (IIoT). The intention behind this is to use more cost-effective, open platforms that also integrate better with an organisation’s information technology (IT) systems. In order to deal with heterogeneity in these systems, middleware platforms such as EdgeX Foundry, IoTivity, FI-WARE for Internet of Things (IoT) systems are under development that provide integration and try to overcome interoperability issues between devices of different standards. In this paper, we consider the EdgeX Foundry IIoT middleware platform as a transformation engine between field devices and enterprise applications. We also consider security as a critical element in this and discuss how to prevent or mitigate the possibility of several security risks. Here we address secure data access control by introducing a declarative policy layer implementable using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Finally, we tackle the interoperability challenge at the application layer by connecting EdgeX with DIME, a model-driven/low-code application development platform that provides methods and techniques for systematic integration based on layered Domain-Specific Languages (DSL). Here, EdgeX services are accessed through a Native DSL, and the application logic is designed in the DIME Language DSL, lifting middleware development/configuration to a DSL abstraction level. Through the use of DSLs, this approach covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable. We validate our approach with an example IIoT use case in smart manufacturing.

Time division multiple access (TDMA) is the medium access control strategy of choice for multihop networks with deterministic delay guarantee requirements. As such, many Internet of Things applications use protocols based on time division multiple access. Optimal slot assignment in such networks is NP-hard when there are strict deadline requirements and is generally done using heuristics that give suboptimal transmission schedules in linear time. However, existing heuristics make a scheduling decision at each time slot based on the same criterion without considering its effect on subsequent network states or scheduling actions. Here, we first identify a set of node features that capture the information necessary for network state representation to aid building schedules using Reinforcement Learning (RL). We then propose three different centralized approaches to RL-based TDMA scheduling that vary in training and network representation methods. Using RL allows applying diverse criteria at different time slots while considering the effect of a scheduling action on meeting the scheduling objective for the entire TDMA frame, resulting in better schedules. We compare the three proposed schemes in terms of how well they meet the scheduling objectives and their applicability to networks with memory and time constraints. One of the schemes proposed is RLSchedule, which is particularly suited to constrained networks. Simulation results for a variety of network scenarios show that RLSchedule reduces the percentage of packets missing deadlines by up to 60% compared to the best available baseline heuristic.

Edge computing and caching of data in the Internet of Things (IoT) has several benefits such as reduced energy consumption by IoT end devices and increased availability of data and Quality of Service (QoS). In typical IoT scenarios, edge nodes (gateways) support several end devices, each of which may produce data in different patterns. In addition, data generated by different types of end devices varies in the application QoS requirements while also widely varying in the data access patterns by IoT services. Managing the data storage resources at edge nodes in such scenarios is a difficult task, especially since the edge nodes themselves may have limited computation capability and storage space. In this paper, we propose a dynamic, differentiated edge cache allocation strategy called NimbleCache that has low computational requirements and performs efficient cache allocation at edge nodes. Based on a Mixture Density Network (MDN), NimbleCache allocates varying portions of the edge cache to traffic of different IoT applications to achieve cache hit ratios very close to the target hit ratio. Simulation results show that NimbleCache achieves good average cache hit ratio with low cache space requirement and small computational overhead.

A recent trend in wireless sensor networks (WSNs) is network virtualization to support on-demand sharing of sensing functionality. The efficient allocation of WSN resources to sensing requests is obtained using virtual network embedding (VNE). This must take into account Quality of Service (e.g., reliability), Quality of Information (e.g., sensing accuracy), and deal with wireless interference. With increased computational complexity due to the added constraints, finding an optimal solution can be prohibitive at scale. We developed an offline embedding algorithm that searches through all possible embeddings, which allowed us to explore the tradeoff between solution quality and search time. We identify a defined set of initial processing steps that lead to high-quality solutions (within 10% of the best solution) in bounded time. We evaluated the algorithm under high stress (large networks with long paths, high data rates, beyond typical WSN configuration) to understand its limitations and the limitations imposed by the underlying WSN substrate.

The requirements of embedded software management-due to concerns about security vulnerabilities or for feature updates in the Internet of Things (IoT) deployments-have raised the need for Firmware Updates Over The Air (FUOTA). With FUOTA's support, security updates, new functionalities, and optimization patches can be deployed with little human intervention to embedded devices over their lifetime. However, supporting FUOTA over one of the most promising IoT networking technologies, LoRaWAN, is not a straightforward task due to LoRaWAN's limitations that challenge bulk downlink data transfer such as a firmware image. Therefore, the LoRa Alliance has proposed new specifications to support multicast, fragmentation, and clock synchronization on top of LoRaWAN, which are essential features to enable efficient FUOTA. In this paper, we review these new specifications and evaluate the FUOTA process in order to quantify the impact of the different FUOTA parameters in terms of the firmware update time, the device's energy consumption, and the firmware update efficiency, showing different trade-offs among the parameters. For this, we developed FUOTASim, a simulation tool that allows us to determine the best FUOTA parameters.

Edge computing allows constrained end devices in wireless networks to offioad heavy computing tasks or data storage when local resources are insufficient. Edge nodes can provide resources such as the bandwidth, storage and innetwork compute power. For example, edge nodes can provide data caches to which constrained end devices can off-load their data and from where user can access data more effectively. However, fair allocation of these resources to competing end devices and data classes while providing good Quality of Service is a challenging task, due to frequently changing network topology and/or traffic conditions. In this paper, we present Federated learning-based dynamic Cache allocation (FedCache) for edge caches in dynamic, constrained networks. FedCache uses federated learning to learn the benefit of a particular cache allocation with low communication overhead. Edge nodes learn locally to adapt to different network conditions and collaboratively share this knowledge so as to avoid having to transmit all data to a single location. Through this federated learning approach, nodes can find resource allocations that result in maximum fairness or efficiency in terms of the cache hit ratio for a given network state. Simulation results show that cache resource allocation using FedCache results in optimal fairness or efficiency of utility for different classes of data when compared to proportional allocation, while incurring low communication overhead.

Automation and data capture in manufacturing, known as Industry 4.0, requires the deployment of a large number of wireless sensor devices in industrial environments. These devices have to be connected via a reliable, low-latency, low-power and low operating-cost network. Although LoRaWAN provides a low-power and reasonable-cost network technology, its current ALOHA-based MAC protocol limits its scalability and reliability. A common practise in wireless networks is to solve this issue and improve scalability through the use of time-slotted communications. However, any time-slotted approach comes with overheads to compute and disseminate the transmission schedule in addition to ensuring global time synchronisation. Affording these overheads is not straight forward with LoRaWAN restrictions on radio duty-cycle and downlink availability. Therefore, in this work, we propose TS-LoRa, an approach that tackles these overheads by allowing devices to self-organise and determine their slot positions in a frame autonomously. In addition to that, only one dedicated slot in each frame is used to ensure global synchronisation and handle acknowledgements. Our experimental results with 25 nodes show that TS-LoRa can achieve more than 99% packet delivery ratio even for the most distant nodes. Moreover, our simulations with a higher number of nodes revealed that TS-LoRa exhibits a lower energy consumption than the confirmable version of LoRaWAN while not compromising the packet delivery ratio.

LoRaWAN promises to provide wide-area network access to low-cost devices that can operate for up to ten years on a single 1000-mAh battery. This makes LoRaWAN particularly suited for the data collection applications (e.g., monitoring applications), where device lifetime is a key performance metric. However, when supporting a large number of devices, LoRaWAN suffers from a scalability issue due to the high collision probability of its Aloha-based MAC layer. The performance worsens further when using acknowledged transmissions due to the duty-cycle restriction at the gateway. For this, we propose FREE, a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN. FREE takes advantage of applications that do not have hard delay requirements on data delivery by supporting the synchronized bulk data transmission. This means data are buffered for transmission in scheduled time slots instead of transmitted straight away. FREE allocates spreading factors, transmission powers, frequency channels, time slots, and schedules slots in frames for LoRaWAN end-devices. As a result, FREE overcomes the scalability problem of LoRaWAN by eliminating collisions and grouping acknowledgments. We evaluate the performance of FREE versus different legacy LoRaWAN configurations. The numerical results show that FREE scales well and achieves almost 100% data delivery and the device lifetime is estimated over ten years independent of traffic type and network size. In comparison to poor scalability, low data delivery and device lifetime of fewer than two years for acknowledged data traffic in the standard LoRaWAN configurations.

Unmanned aerial vehicles (UAVs) have attracted a lot of attention due to their enormous potentiality in civil and military applications over the past years. In order to allow accurate control action of UAV, a robust and real-time state estimation technique is required. In this paper, we propose a Kalman filter based UAV state estimation technique when the communication takes place over wireless links in an Internet of Things (IoT) network. We consider that a set of sensors observes the state of the UAV and transmits the observation to a control center (central server) over a distributed wireless IoT network. To deal with the communication impairments due to wireless communication links between the UAV's sensors and the IoT system components, e.g., IoT gateways, a Bose-Chaudhuri-Hocquenghem coded communication system is presented. Based on the received signals at the IoT gateways, a global state estimation technique is proposed. Performance of the proposed communication and estimation scheme is demonstrated through numerical results for different conditions. From the comparison with a conventional estimation scheme, it is observed that the proposed scheme significantly outperforms the conventional scheme in terms of state estimation and error performance.

Header compression is the key to adapting long header protocols like IPv6 for transmission using short frame size network protocols such as are used in Low Power Wide Area Networks (LPWANs). In LPWANs only one or two octets may be available to transmit upper layer protocol headers, which makes known header compression mechanisms unsuitable. Static Context Header Compression (SCHC) has recently been proposed to address the LPWANs limitations. SCHC works under the assumption that LPWANs are preprogrammed with known data flows, thus uses a static context. However, this assumption is not always valid in an Internet of Things (IoT) context, where devices should be accessible from any IPv6 address. Therefore, in this paper we propose a dummy mapping technique that can effectively compress/decompress some header fields of unknown flows. The dummy mapping creates a fixed size list of dummy values that are dynamically mapped at the gateway compressor to the values of headers fields. The mappings stay alive to compress/decompress these fields until their flow ends, and then the same dummy values can be remapped to different header fields of different flows. The Dummy mapping exploits the SCHC framework and does not require resynchronization to meet the limitations of LPWANs. Here, we explore the impact of the dummy mapping size, the request arrival rate and the LPWAN link layer parameters on the compression efficiency.

In the 2.4 GHz unlicensed spectrum, the coexistence of WiFi, Bluetooth and IEEE 802.15.4 devices generates increased channel contention. Notably, low-power wireless networks experience packet loss and delays due to interference. To improve the performance of low-power wireless networks under interference, we propose a data driven proactive approach based on interference modeling for white space prediction. We leverage statistical analysis of real-world traces from two indoor environments characterized by varying channel conditions to identify interference patterns. We characterize interference in terms of Inter-Arrival Time (IAT) and number of interfering signals and use a Gaussian Mixture Model (GMM) to accurately estimate the interference distribution as observed by the low-power wireless nodes. Then, we use a Hidden Markov Model (HMM) for white space prediction. Our validation w.r.t. real-world traces from two environments show that our GMM model can estimate interference with an accuracy higher than 94:7%. Moreover, the white space prediction evaluation shows an average accuracy of 97:7% and 89:5% across the two environments.

In this paper, We analyze the performance of popular low-power wide-area networking technology called long rage (LoRa) using different data traffic generation models, and by varying the number of gateways in a LoRa network cell. Moreover, we also analyze LoRa's performance in the presence of multiple concurrent applications in a LoRa network. Here, we also present an extension for an existing LoRa simulator called LoRaSim to simulate multiple concurrent applications in the presence of multiple gateways in a LoRa network. Our results demonstrate that, a LoRa communication setting that supports diversity in terms of bandwidth, spreading factor, and coding rate demonstrates good performance for different data traffic models mostly without requiring multiple gateways in a LoRa network cell. Through simulation case studies, we also demonstrate effectiveness of our extended LoRaSim simulator.

Low-power wide-area networking (LPWAN) has gained much attention recently and offers significant potential to support a large number of Internet of Things (IoT) applications. For device simplicity, LPWANs tend to use a simple channel access control protocol such as Aloha, which impacts performance. While several LPWAN technologies are available, we specifically focus on the Long Range (LoRa) in this paper. Our goal in this study is to search for a channel access control protocol in conjunction with the LoRa physical layer that can improve network performance in terms of reliability, throughput, energy consumption, and yet retain simplicity. We analyze a range of channel access control protocols, such as pure Aloha, delay before transmit, random frequency hopping, and carrier sense multiple access (CSMA). Our experiments use available periodic and event-based data traffic generation models for Internet of Things applications. Our results show that, CSMA and random frequency hopping demonstrate significantly better performance for both periodic and event-based data traffic models. Moreover, CSMA also exhibits scalability features in terms of the number of nodes in a network and data traffic generation models.

In shared wireless sensor networks (WSNs), multiple users request access to sensing resources, often with varying sampling rates and QoS requirements. To accommodate a request, appropriate sensing, computing and communication resources need to be allocated across the network. Traditionally, each request is mapped to a dedicated set of resources, even when the requests are similar. The need to reduce resource usage has led to data virtualization techniques that focus primarily on merging the requests ignoring the QoS requirements. In this paper, we present a QoS-aware resource allocation approach, JudiShare, that merges requests, where possible, if they are compatible in their requirements, providing judicious reuse of both sensing and communication resources through a mixture of data virtualization and Virtual Network Embedding (VNE). We show that JudiShare respects QoS requirements and reduces resource usage to up to 60%. This, in turn, allows up to 50% more requests to be accommodated onto the network, even when the network resources are fully utilized.

Wireless sensor systems are becoming increasingly attractive as a means to flexibly extend sensing capabilities towards more intelligent cars. Wireless sensors can reduce cabling costs and weight, allow for cheaper customization, and allow retrofitting new functionality into older vehicles. The communication system design for these wireless sensor systems requires models for radio propagation in cars. In this paper we present a novel empirical path loss model for the 2.4 GHz based IEEE 802.15.4 radio channel. Our model is based on extensive measurements in two compact cars. We have extracted model parameters for two types of wireless links, those between sensors in the car and those towards sensors on the outside of the car. We have calibrated the model parameters for both path loss and shadow fading to fit our measurements.

In this paper, we present results of a study of the data rate fairness among nodes within a LoRaWAN cell. Since LoRa/LoRaWAN supports various data rates, we firstly derive the fairest ratios of deploying each data rate within a cell for a fair collision probability. LoRa/LoRaWan, like other frequency modulation based radio interfaces, exhibits the capture effect in which only the stronger signal of colliding signals will be extracted. This leads to unfairness, where far nodes or nodes experiencing higher attenuation are less likely to see their packets received correctly. Therefore, we secondly develop a transmission power control algorithm to balance the received signal powers from all nodes regardless of their distances from the gateway for a fair data extraction. Simulations show that our approach achieves higher fairness in data rate than the state-of-art in almost all network configurations.

Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz unlicensed spectrum causing packet losses and increased channel contention. In particular, WiFi interference is a severe problem for low-power wireless networks causing a significant degradation of the overall performance. We propose here a proactive approach based on WiFi interference modeling for accurately predicting transmission opportunities for low-power wireless networks. We leverage statistical analysis of real-world WiFi traces to learn aggregated traffic characteristics in terms of Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of interference. We further use a hidden Markov model (HMM) for channeloccupancy prediction. We evaluated the performance of: i) the MMPP(2) traffic model w. r. t. real-world traces and an existing Pareto model for accurately characterizing the WiFi traffic and, ii) compared the HMM based white space prediction to random channel access. We report encouraging results for using interference modeling for white space prediction.

Encouraged by the success of network virtualization in enterprise networks, wireless sensor network (WSN) virtualization has been receiving increasing attention. WSN virtualization is seen as offering economic benefits through resource sharing. But the actual gain is not clear and its cost-benefit ratio has not been thoroughly analyzed. While the discussion about the economic benefits of network virtualization is often reduced to two key components, namely CAPEX and OPEX, the effect of radio interference in wireless multi-hop networks such as WSN has not been considered. In this paper, we analyze the influencing factors that contribute to the cost of providing a virtual WSN over a shared physical substrate. We model the CAPEX and OPEX associated with Virtualized WSNs (VWSNs) and introduce an additional element, which we call INEX, into our cost function which captures the future impact (i.e. the ”actual cost”) of interference. INEX results in a loss of income due to increased VN request rejection rates. Using this model we conduct extensive simulations that allow us to compare the cost difference between renting shared WSN resources and deploying a standalone network. The results indicate that shared WSNs result in higher cost when there is only one sink.

Energy consumed by computation and cooling represents the greatest percentage of the average energy consumed in a data centre. As these two aspects are not always coordinated, energy consumption is not optimised. Data centres lack an integrated system that jointly optimises and controls all the operations in order to reduce energy consumption and increase the usage of renewable sources. GENiC is addressing this through a novel scalable, integrate energy management and control platform for data centre wide optimisation. We have implemented and prototype of the platform together with workload and thermal management algorithms. We evaluate the algorithms in a simulation based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management.

We present a technique to identify transmission timing for IEEE 802.15.4 based Wireless Sensor Networks (WSNs) in the presence of WiFi interference. Our technique is based on modeling WiFi traffic with a Modulated Markov Poisson Process (MMPP) model in order to enable us to predict when WiFi transmissions take place and avoid them. We have evaluated the accuracy of our model in a small test-bed. Results are promising and suggest that our approach can increase the reliability of IEEE802.15.4 transmissions.

The increased introduction of individually wirelessly controlled LED light sources in conjunction with the need to retrofit those into existing buildings often leads to very dense wireless lighting networks. Current approaches for control message transmission in such systems are based on broadcasting messages among the many luminaires. However, adequate communication performance, in particular, sufficiently low latency and message reception synchronicity, is difficult to ensure in such networks. This paper introduces a novel data dissemination protocol for such networks, which makes use of the recently introduced IEEE802.15.4e TSCH mode. The analysis of our protocol shows that it can fulfil the requirements for dense wireless lighting control networks in achieving adequately low message delivery latency, high reliability and fulfilling user expectations.

Previous research has shown that centralized network control in Wireless Sensor Networks (WSNs) can lead to improved network lifetime, benefit reliability, help to diagnose and localize network failures, assist network recovery, and lead to optimal routing and transmission scheduling. A stepping stone to centralized network control is to build and maintain a complete network topology model that scales and reacts to the network dynamics that occur in low-power wireless networks. We propose SMOG as a mechanism to build and maintain a centralized full network topology model using probabilistic data structures. Extensive analysis of the proposed approach in both simulation and two testbeds shows that SMOG can build a complete model of a WSN of over 100 nodes with 98% accuracy in less than four minutes. Our approach also offers fast recovery from heavy network interference, recovering model accuracy to 98% in less than two and a half minutes.

The concept of Intelligent Transportation Systems (ITS) presents new R\&D challenges in the transportation and ICT sectors and is currently receiving considerable interest from the research community. The primary objective of ITS is the creation of advanced road traffic systems for improved traffic safety, efficiency, and travelling comfort. Applications such as trip planning, automatic tolling and emergency warnings, among others, are envisaged in a system which can potentially reform modern transportation. Basic vehicle and roadside infrastructure collaboration allows an increase in efficiency and safety and acts as the foundation for an extensive application set to achieve these ITS goals. There are some important considerations however; communications are the nervous system of ITS and the major challenge lies in ensuring ITS services are adequately supported by communication services. This paper highlights the issue of service and communications management in cooperative vehicular networks to ensure an "Always Satisfactorily Connected" (ASC) objective. Using the CALMnet simulation environment, a set of selection objectives following ASC and "Always Best Connected" (ABC) are examined and compared. Results highlight the impact of changing objectives in the cooperative vehicular environment and confirm that ASC most adequately meet service requirements.

In-vehicle wireless sensor/actuator networks are subject to QoS requirements for real-time performance. We show how these requirements can be met, especially for randomly distributed traffic, by our improvements of the Soft Real-Time Shared Time Slot (SRTST) MAC protocol. The improvements target the upper delay limit of prioritized messages and achieve a bounded delay within the delay requirements for such messages. We performed simulations with the discrete event simulator OMNeT++ of our improvements and compared them to the original MAC protocol.

This paper provides an overview of the architecture for self-organizing, co-operative and robust Building Automation Systems (BAS) proposed by the EC funded FP7 SCUBA1project. We describe the current situation in monitoring and control systems and outline the typical stakeholders involved in the case of building automation systems. We derive seven typical use cases which will be demonstrated and evaluated on pilot sites. From these use cases the project designed an architecture relying on six main modules that realize the design, commissioning and operation of self-organizing, co-operative, robust BAS.

There is a strong push towards smart buildings that aim to achieve comfort, safety and energy efficiency, through building automation systems (BAS) that incorporate multiple subsystems such as heating and air-conditioning, lighting, access control etc. The design, commissioning and operation of BAS is already challenging when handling an individual subsystem; however when introducing co-operation between systems the complexity increases dramatically. Balancing the contradictory requirements of comfort, safety and energy efficiency and coping with the dynamics of constantly changing environmental conditions, usage patterns, user needs etc. is a demanding task. This paper outlines an approach to the systematic engineering of cooperating, adaptive building automation systems, which aims to formalize the engineering approach in the form of an integrated tool chain that supports the building stakeholders to produce site-specific robust and reliable building automation.

Building Automation usually involves a large number of systems that should cooperate in order to improve e.g. the user comfort, security, but also decrease the overall energy consumption. One aim of the EU funded SCUBA project is to improve the coordination among devices and systems installed in a building. This paper deals with the extension of a framework dedicated to Building Automation and built on top of the LINC resource-based middleware. Two particular tools developed in SCUBA and that make use of the middleware are also presented together with the encapsulation of the ontology-based Building Automation System model named BASOnt.

In this paper we present occupancy estimation techniques using real (motion, door closure) and virtual (PC activity detector) sensors. The techniques considered here are based on the decision tree and artificial neural network models. Results from an experimental test-bed in a four person office room are also presented.

With the increasing demand for traffic safety and efficiency and constant search for innovative solutions within the automotive market coupled with supporting initiatives from regulatory domains, the potential of Intelligent Transportation Systems (ITS) is immense. Basic vehicle and roadside infrastructure collaboration allows an increase in efficiency and safety and acts as the foundation for an extensive application set to achieve the ITS goals of cleaner, safer and more efficient travel. There are some important considerations however. Taking into account the wide array of communication technologies and plethora of proposed applications, this paper aims to address one of the major and largely unexplored challenges facing the ITS research community in relation to service and communication management (SCM), whereby the underlying communications capability is sufficiently exploited to assure satisfactory operation of deployed ITS applications. A complete SCM solution is proposed under an “Always Satisfactorially Connected” (ASC) objective; two probing techniques are examined to assess the performance of the candidate communication networks and a Grey Relational Analysis (GRA) based selection policies are considered. The performance of the proposed SCM schemes is evaluated using CALMNet, a comprehensive network-centric simulation environment for CALM-based cooperative vehicular systems. Results highlight the effect of different techniques on system performance and user satisfaction.

Many of the physical systems in the real world together with their surrounding environments and the networks used for monitoring these systems can be mathematically modeled as a graph. We present a graph-based optimization technique for source localization in such systems. The proposed technique uses the detection times of any particular event or phenomenon of interest at different measurement points within the system to determine the actual location or source of the event. The graphical model represents the propagation characteristics of the physical phenomenon as well as the topology of the monitoring network. The source localization algorithm is validated using experimental data from a wireless sensor network test bed used for monitoring the drinking water distribution system. We also present a method for analyzing the sensitivity of the localization result to estimation errors in the parameters of the graphical model.

The current growth in embedded ICT infrastructure, driven by visions such as the "Smart Cities" concept, is leading to the deployment of a wide range of embedded systems in our environment, which motivates the need for a reusable, flexible and manageable Wireless Sensor Network or WSN infrastructure. However, to simplify the system operation and maintenance as well as to reduce costs, WSNs mus become an infrastructure that is capable of providing services to multiple end users concurrently, rather than having to roll out individual infrastructures for specific purposes. Here, we present the concept of a WSN infrastructure as a WSN Cloud, which provides services to multiple application and data collection systems which adheres to the cloud computing paradigm. Each instance of the WSN Cloud (i.e. a specific set of services configured by a particular end user/system) utilises the WSN infrastructure as if it was a unique network provisioned for their specific requirements. This realisation of the WSN Cloud as Network as a Service or NaaS requires the WSN to support a service orientated software architecture allowing other systems to provision the WSN infrastructure for their needs with NaaS allowing multiple systems to use the WSN uniquely and concurrently. The WSN-Service Orchestration Architecture "WSN-SOrA" presented here is a novel approach to orchestrate service provisioning for embedded networked systems, and enables WSNs to act as cloud ready infrastructures that facilitate on-demand provisioning for potentially multiple individual backend systems.

Traditionally, transmission power is adjusted dynamically to overcome unreliability over lossy links in energy-constrained Wireless Sensor Networks (WSNs). The network node increases its transmission power to achieve immunity against link errors or lowers the power to save energy and prevent interference. Through systematic analysis, we illuminate that it adversely affects the channel contention, network throughput and energy consumption at network scale. Therefore, we implement a novel but effective Fuzzy Inference based Delay and Channel aware Communication (FI-DACC) mechanism at WSN nodes which employs lightweight Forward Error Correction (FEC) codes at low transmission power. Besides selecting an adequate FEC code, the proposed strategy also tunes MAC layer back off duration to prevent packet collisions. To simplify the decision process, a cascaded structure of two fuzzy logic controllers is formulated that undertakes heterogeneous parameters into account. We integrate our solution into IEEE802.15.4 based energy constrained WSN scenarios and evaluate the scheme from real-time packet delivery viewpoint. Results indicate that the proposed approach enhances network real-time capacity with low energy overheads as compared to alternative schemes, for e.g. Real-time Power Routing (RPAR), adaptive power control, hybrid, Interference-aware Transmission Power Control (I-TPC) [4].

Sensor networks used for event detection scenarios are considered where the goal is to detect any abnormal conditions with minimum possible delay. We propose a greedy heuristic based sensor selection and sequential detection procedure that significantly improves the detection speed, measured in terms of the number of measurements needed for detection, and is suitable for distributed implementation. In the proposed model, the fusion center selects one sensor at a time for measurement while maximizing a greedy heuristic. Instead of collecting a fixed number of measurements, the fusion center collects one measurement at each time step, until by a sequential decision rule the collection stops and a decision is made. The sequential procedure significantly outperforms a non-sequential (or fixed sample size) detector in that it always needs fewer measurements on average to achieve the same detection performance.

The commissioning of sensors and actuators within a building is often carried out by an operator who manually gathers and later inserts configuration data into the building management system. Data sets collected can easily reach hundreds which makes this manual process a slow complex operation which in turn is prone to errors. In this paper, we present a client-server architecture for a simple web based commissioning application, based on the Constrained Application Protocol (CoAP), that allows an operator to easily discover and browse through newly installed devices in order to perform commissioning configurations onsite.

Wireless senor networks (WSNs) for building automation are a low cost solution in terms of installation and retrofit. WSN provide building operators with the opportunity to monitor and control building performance to improve efficiency by becoming more energy usage aware and demand responsive. However, the penetration of wireless sensing technology has been hampered by concerns regarding the reliability and manageability of wireless systems in harsh operating environments. The traditional solutions to address WSN reliability and manageability are to employ high levels of node redundancy and to embed self-management functions within communications protocols themselves. However the disadvantages of this approach are cost and non-optimum behaviour in large scale systems. The main motivation for a building operator to deploy a WSN is cost reduction and hence the costly requirement for high levels of node redundancy is unlikely to provide a satisfactory solution. WSNs deployed for building monitoring, unlike a typical communications network, is part of a broader building management business designed to curtail operational overheads for an enterprise. For buildings the physical deployment of a building management system is likely to be a once off roll out. However, the internal layout of the building is often dynamic as the traditional role of building owner/user has shifted and it is common practice now for several companies to lease space within the one building. With a once off BMS deployment the installation and use of the network tended to be completely independent activities. It is now well accepted that a continuous commissioning approach to building operation is needed to maintain optimum building performance. We argue that this also needs to extend to the wireless sensing infrastructure, creating demand for a continuous wireless infrastructure (wi) design, deployment and reconfiguration lifecycle process that optimises the wireless infrastructure aspects of the BMS. We propose Wi* an innovative solution for the design and management of wireless sensing infrastructure capable of interfacing with the BMS to provide an integrated technology platform for fine grained building automation.

The design and deployment of a wireless sensor network (WSN) for building automation applications is a complex operation that requires expert knowledge and experience. This paper presents an evaluation of a WSN deployment support framework for in-building wireless infrastructures. A case study consisting of a sample network deployment for environmental monitoring is used to investigate the need for such support tools. The network infrastructure design suggested by the deployment support tool is compared against designs done using basic planning guidelines and a design based on an extensive site survey and experience. It will be shown how the deployment support tools provide a WSN with a reduced infrastructure cost and improved sensing packet delivery ratio when compared to the designs using traditional approaches.

As wireless embedded systems become more and more common and used across many application domains there is a need for modeling and design tools to support the deployment process. Although a significant amount of research has been carried out in the area of protocol design, middleware and energy, packaging and embedded systems design, there remains a lack of support tools for designers and system integrators when deploying complex indoor wireless infrastructures (Wi) to support site specific applications. In this paper we present a modeling tool known as Wi-Design that was developed to provide deployment support for engineers and system integrators when planning a wireless sensor infrastructure with particular focus on in building wireless applications. We show how the tool can simplify the deployment process and provide enhanced confidence in wireless deployments.

This paper proposes an extension to the current IEEE802.15.4 standard in which flexible mesh networking in the low power beacon mode is enabled through the use of a distributed duty cycle management (DDCM) strategy in conjunction with a distributed time beacon scheduling (DTBS) technique. The DTBS technique enables a beacon enabled coordinator to calculate its time schedule to transmit beacons, thus avoiding potential collisions, without the need of a central coordinator. The proposed DDCM strategy is able to re-configure, in an IEEE802.15.4 beacon-enabled wireless mesh sensor network, nodes' duty cycle based only on locally available network information. DDCM features the allocation, deallocation and reallocation of superframe slots as well as the prevention and resolution of slot conflicts within a mesh network in a distributed fashion. The feasibility of our proposal is demonstrated and evaluated through network simulations. The proposed DDCM algorithm shows low slot collision probability and better energy efficiency than current state-of-the-art approaches.

In populated indoor environments, the radio signal quality is heavily influenced by channel impairments caused by movement of people, obstacles, or radio interferences. In such environments, high packet drop rates lead to frequent retransmissions and increased energy consumption of resource-restricted wireless nodes. To overcome this, we propose a novel Forward Error Correction (FEC) based adaptive error control strategy that employs a cascaded fuzzy inference system to combat communication unreliability. The strategy unifies various heterogeneous metrics such as signal-to-noise ratio, line-of-sight/non-line-of-sight detection and ACK/NACK to closely estimate the low-power links' quality and based on that, selects an appropriate FEC code to protect packet transmissions. Numerical evaluations are carried out using a realistic indoor fading channel model and IEEE 802.15.4 2.4 GHz modulation format. The performance results obtained from a comparative analysis with static and Adaptive FEC Code Control schemes (AFECCC) conclude that the proposed adaptive scheme guarantees better trade-off (packet error rate and energy-efficiency) for indoor WSN applications.

In indoor environment, the IEEE 802.15.4 low-rate, low-power packet transmissions experience statistical correlation between channel errors due to interference and multipath fading. The 802.15.4 MAC layer implements an ARQ (Automatic Repeat-Request) mechanism for error recovery. In this work, we investigate the impact of correlated errors on the ARQ mechanism. The MAC layer back-off strategy employs a uniform random distribution to choose back-off values; which often reduces retransmission resolution time when packets confront collisions or transmission errors. We therefore propose the application of non-uniform (skewed) distribution which improves the system performance. Furthermore, an adaptive back off strategy, fastEI-slowED is developed for IEEE 802.15.4 MAC protocol to combat channel errors in time varying conditions. A detailed comparative analysis is presented employing accurate models of wireless channel and MAC protocol. Numerical results indicate that proposed scheme performs better, especially in worst channel conditions, when long duration error bursts are more frequent.

The complex nature of wireless local area networks (WLAN) design has led many of the deployments being done in an ad-hoc fashion without efficient design methodologies. Although this approach may work for a small environment with a small number of access points, it is infeasible to use such a process when designing a larger wireless infrastructure. Due to the low cost that is indicative of WLAN deployments, many practitioners view formal optimization techniques as being too complex and costly to implement. There have been a number of research works that investigate the use of formal optimization techniques for the accurate design of a WLAN. Unfortunately, the approaches taken do not address one major issue when designing a complex and demanding wireless network infrastructure, namely scalability. An optimization algorithm must consider a multitude of design criteria and therefore needs to be scalable to be successfully applied to large scenarios. The main contribution of the work presented in this paper is the development of a scalable optimization algorithm based on the tools of distributed artificial intelligence, which overcomes the failings of current approaches and can be utilized for WLAN design regardless of size or complexity of site specific requirements.

With recent advances in vehicular technology, a variety sensors, radars and onboard computing systems have enabled vehicles to become powerful information gathering and processing platforms. Sensors can continuously monitor and interpret the vehicle's local environment and quickly detect dangerous situations. As vehicles in close proximity detect the same dangerous situation they will inevitably broadcast messages relating to the same event. As all these vehicles report on the same event broadcasting leads to dramatically excessive message redundancy. In the paper we present the Event Suppression for Safety Message Dissemination (ESSMD) scheme that reduces the number of broadcasting vehicles reporting on the same event. This scheme was compared with existing aggregation strategies for safety-related message dissemination. Experimental results using the OPNET simulator demonstrate that ESSMD significantly reduces redundant data transmissions and does not add any extra end-to-end delay compared with existing aggregation strategies. However, ESSMD had a decreasing probability of event reception due to an unreliable broadcasting protocol. The reception rate was improved by the introduction of a scheme called ESSMD+Rep that increases the reliability of the broadcasting protocol by repeating broadcasts at the source vehicles.

Vehicular communication is regarded as a major innovative feature for in-car technology. While improving road safety is unanimously considered the major driving factor for the deployment of Active Safety Intelligent Vehicle Safety Systems, the challenges relating to reliable multi-hop broadcasting are many in vehicular networking. In fact, safety applications must rely on very accurate and up to date information about the surrounding environment, which in turn requires the use of accurate positioning systems and smart communication protocols for exchanging information. Communications protocols for vehicular ad hoc networks (VANETs) must guarantee fast and reliable delivery of information to all vehicles in the neighbourhood, where the wireless communication medium is shared and highly unreliable with limited bandwidth. In this paper, we present a geo-broadcasting extension to the Slotted Restricted Mobility-Based (SRMB) broadcasting protocol and compare it with existing geobroadcasting protocols in highway environments.

Mobile devices are rapidly evolving beyond their original design specification. With the inclusion of innovative sensing and actuating facilities, the potential application space of these new devices is huge. The challenge now becomes harnessing the abilities' of these instruments in innovative ways. It is the aim of this paper to provide a unique service rich architecture integrating some of these novel sensing capabilities (i.e. GPS, accelerometer, gyroscope) to provide a potential "killer application" in the location and tracking space. Existing tracking systems in the mobile devices area are quite invasive for the user and labour intensive from a commercial perspective. The architecture proposed here is much more transparent, can be entirely automated and incorporates a Rich Internet Architecture (RIA) approach, a first for this type of scenario.

In this paper we investigate the impact of a relay node (RN) placement plan on a heterogeneous wireless network called hybrid wireless network with dedicated delay nodes (HWN*) in terms of system capacity, transmission delay and quality of service (QoS). A novel HWN* heuristic RN placement (HHRP) algorithm, which considers RN assistance & support for radio resource sharing such as inter-network traffic balancing, TD/CDMA soft handover, routing and user mobility has been implemented. A system level simulation is used to quantify the benefits of HWN* using HHRP algorithm over HWN* with other RN placement plans and multi-hop cellular networks.

The complex nature of Wireless Local Area Network (WLAN) design, especially when designing large scale networks underlines the need for an automatic WLAN planning tool. The current approach to WLAN design is ad hoc and can lead to an adverse affect on network and service quality. There has been significant research into optimisation techniques and planning tools, many of which use a centralised optimisation approach that is not scalable especially when designing large scale WLAN. The research presented in this paper proposes an approach to WLAN design that is fully distributed based on a scalable optimisation technique. The proposed approach uses elements of Artificial Intelligence and Game Theory to design a viable WLAN regardless of the environment size.

Cooperative systems in transportation can bring new intelligence for vehicles, roadside systems, operators and individuals by creating a communications platform allowing vehicles and infrastructure to share information. The performance of this underlying communication system has a major impact on the effectiveness of the emerging applications for intelligent transportation systems. Similarly, the approach taken to the dissemination of relevant information throughout the vehicular setting is influenced by the network performance characteristics. This paper investigates the concept of data dissemination in a heterogeneous vehicular wireless environment. A communications architecture which consists of infrastructure based transmission for cooperative vehicular systems is described. Following this, a simple, policy-based solution to establish how best to disseminate the data for an envisaged ITS application is presented. This policy considers the application requirements and the quality of the wireless carrier in determining how the information can be propagated to the relevant recipients in the most effective and efficient manner.

This paper presents the agent grid service marketplace (AGSM), a novel framework that allows dynamic service provision in next generation wireless systems. The AGSM will allow mobile service and content providers to register, discover and compose services by forming virtual organization and to sell these services to business and retail customers on a per request basis. It implements the system as a grid based market place where service providers are represented as software agents incorporating a reasoning engine provided by the well known belief, desire, intention model. The services purchased within the marketplace are invoked and executed by the service provider agent providing an economic based agent grid resource broker.

The rapid increase in the use of IEEE 802.11 wireless local area networks (WLAN) for a diverse range of applications, has introduced an increased complexity into WLAN design, as regards to accurate access point (AP) position assessment, which can severely impact the performance of large scale WLANs. This paper presents two approaches to WLAN design that allows the designer to describe where the WLAN is to be deployed and define the design requirements based on signal coverage and usage. Both methods automatically optimise and suggest a WLAN design that satisfies the user requirements. Both approaches have been implemented and evaluated based on signal coverage and maximum achievable throughput.

The increasing complexity and dynamics of logistic processes is creating significant new challenges for the management of goods transport. This is leading to increased requirements for the routing of goods and transport vehicles in order to adapt to the dynamics of the changing logistics environment. Current practice of vehicle and goods routing is based on centralised planning and control. This approach is now rapidly becoming too inflexible and complex for maintaining efficient goods transport. In this paper we introduce a novel routing process which implements distributed decision making among transport vehicles, goods items, and other entities in a logistic transport network. The proposed approach enables packages (goods items) and transport vehicles to find their routes autonomously whilst reacting to dynamic changes in their environment.

The Telecommunication Service Exchange is a communication service platform based on a digital marketplace concept that enables customers to purchase telecommunication services. Customers are able buy product through this platform just as in a supermarket without being compelled to buy services from a particular producer or service provider. To enable this type of service provision, the current subscription model in telecommunications needs to be modified allowing customers to purchase telecommunication services on a per call basis. Using SIP, Electronic-Marketplaces and Agents, this paper outlines the architecture to achieve this as well as the possible benefits that this presents for both service providers and customers. An evaluation of the performance of the platform is also provided.

TelecomSupermart is a 'Win-Win' solution for users and providers of communication services. In current communication systems, wired and wireless, subscribers are more or less tied to their service providers by means of long-term contracts - the business model is therefore rigid. In an ideal scenario, users should be free to buy telecommunications services just as they are able to buy other commodities in a super market without being obliged to buy products from a particular company. The service providers should also be able to maximize their revenues by means of intelligent strategies such as dynamic pricing based on demand and supply trends in the market. TelecomSupermart is a novel implementation of an agent-based system for telecommunication service provision. It encompasses the merits of an open and free market and facilitates revenue maximization for service providers while giving the users the choice of selecting a service provider based on their own requirements. The backbone of the TelecomSupermart is a decentralized network of individual service providers offering entrepreneurs and individuals an opportunity to become a listed service provider and offer communications services through the TelecomSupermart.

With the evolution toward 2.5G bringing a wide range of new services, it is expected that the tele-traffic demand on current GSM and IS136 networks will further increase. In this paper we propose a new pro-active resource allocation method of increasing cellular network capacity by introducing an adaptive radio resource management system into a typical GSM/IS136 network. Adaptation is performed by using neural networks (NNs) to predict each cells future resource demands and adjusting the available resources accordingly. Results are presented which exhibit less resource requirements than existing fixed channel allocation (FCA) networks and performance that is comparable to previously proposed dynamic resource allocation (DRA) schemes, but with the advantage of significantly less complexity and no additional network signaling load.