Internet of Things (IoT) networks require regular firmware updates to ensure enhanced security and stability. As we move towards methodologies of codifying security and policy decisions and exchanging them over IoT large-scale deployments (security-as-a-code), these demands should be considered a routine operation. However, rolling out firmware updates to large-scale networks presents a crucial challenge for constrained wireless environments with large numbers of IoT devices. This paper initially investigates how the current state-of-the-art protocols operate in such adverse conditions by measuring various Quality-of-Service (QoS) Key Performance Indicators (KPIs) of the shared wireless medium. We later discuss how Concurrent Transmissions (CT) can extend the scalability of IoT protocols and ensure reliable firmware roll-outs over large geographical areas. Measuring KPIs such as the mesh join time, the throughput, and the number of nodes forming a network, we provide great insight into how an IoT environment will behave under a large-scale firmware roll-out. Finally, we conducted our performance investigation over the UMBRELLA platform, a real-world IoT testbed deployed in Bristol, UK. This ensures our findings represent a realistic IoT scenario and meet the strict QoS requirements of today’s IoT applications.

This paper provides details of the collaborative robotics testbed platform that has been developed within the UMBRELLA project. The testbed is part of a larger open Industrial IoT testbed which is currently being deployed in the UK. The aim of the testbed is to permit flexible experimentation using different end devices (including smart city sensing and robot nodes) to evaluate algorithms or new practical application scenarios. For the collaborative robotics testbed this relates to warehouse robotics, which can move pallets of different sizes and shapes. The testbed also includes simulator facilities for validation of algorithms prior to deployment on the robot nodes. The nodes support a rich set of sensors, actuators, and wireless communication technologies. The software architecture is based on Docker containers and ROS2 DDS middleware for flexible and extensible evolution to support future sensors or network technologies. This will be provided as an open testbed to support research, experimentation and evaluation of swarm robotics and other Industrial IoT use-cases.

In recent years, there has been considerable interest in computational offloading algorithms. The interest is mainly driven by the potential savings that offloading offers in task completion time and mobile device energy consumption. This paper builds on authors' previous work on computational offloading and describes a multi-objective optimization model that optimizes time and energy in a network with multiple Multi-Access Edge Computing servers (MECs) and Mobile Devices (MDs). Each MD has multiple computational jobs to process, and each task can be processed locally or offloaded to one of the MEC servers. Several heuristic offloading policies are proposed and tested with an objective function with a range of weightings for optimizing time and energy. The approaches are illustrated with the help of three test cases of varying complexity. The objective function shows a continuous variation as the emphasis is placed on either time or energy saving by the weighting factors. The numerical tests demonstrate that the proposed heuristic algorithms produce near-optimal computational offloading solutions while considering a combined weighted score for schedule task completion time and energy.

Graceful degradation is a potential advantage of Multi-Robot Systems over Single-Robot Systems. In aerial robotics applications, such as infrastructure inspection, this trait is desirable as it would improve mission reliability despite the use of failure-prone low-cost drones. The Reliability-Aware Multi-Agent Coverage Path Planning (RA-MCPP) problem finds path plans for each robot to maximise the probability of mission completion by a given deadline. This paper proposes a path planner for RA-MCPP formulated in continuous time, enabling more complex realistic environments to be considered. The proposed method (i) extends a reliability evaluation framework to evaluate the Probability of Completion metric on asynchronous strategies on non-unit lattice graph environments, and (ii) introduces a greedy-genetic meta-heuristic optimisation method as a scalable and accurate RA-MCPP solver. This method is shown to provide plans with higher reliability when compared with existing approaches in three real inspection scenarios.

Automated mood recognition has been studied in recent times with great emphasis on stress in particular. Other affective states are also of great importance, as studying them can help in understanding human behaviours in more detail. Most of the studies conducted in the realisation of an automated system that is capable of recognising human moods have established that mood is personal—that is, mood perception differs amongst individuals. Previous machine learning--based frameworks confirm this hypothesis, with personalised models almost always outperforming the generalised methods. In this article, we propose a novel system for grouping individuals in what we refer to as “perception clusters” based on their physiological signals. We evaluate perception clusters with a trial of nine users in a work environment, recording physiological and activity data for at least 10 days. Our results reveal no significant difference in performance with respect to a personalised approach and that our method performs equally better against traditional generalised methods. Such an approach significantly reduces computational requirements that are otherwise necessary for personalised approaches requiring individual models developed separately for each user. Further, perception clusters manifest a direction towards semi-supervised affective modelling in which individual perceptions are inferred from the data.

In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions.

Computational offloading is a strategy by which mobile device (MD) users can access the superior processing power of a Multi-Access Edge Computing (MEC) server network. In this paper, we contribute a model of a system that consists of multiple MEC servers and multiple MD users. Each MD has multiple computational tasks to perform, and each task can either be computed locally on the MD, or it can be offloaded to one of the MEC servers. For this system and having global knowledge, we compute the theoretical optimal allocation that minimises the time required to complete the computation of all tasks. Subsequently, we contribute a distributed heuristic algorithm that allows each MD to independently, and using local knowledge only, decide how to handle each individual job. Furthermore, we propose three approaches to decide whether to offload each individual job, and three mechanisms to determine which MEC server each task should be offloaded to. We use simulations to evaluate those approaches in terms of how well they can approximate the theoretical optimum. The proposed heuristic algorithm is tested on a range of experiments, and the results demonstrate that the heuristic algorithm can produce reasonable quality solutions.

Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.

RPL, the de-facto standard for low power and lossy networks, forms multi-hop routing structures between network nodes and a single root. It intrinsically minimises the number of hops across the mesh, however this can result in large distances between adjacent forwarding nodes. Consequently, during forwarding operations, the received signal strength can be close to the receiver sensitivity threshold and result in frequent packet losses due to link fluctuations. RECLAIM, our proposed approach, overcomes this challenge by transmitting routebuilding messages at a reduced power level at first while switching back to the normal transmission power level during the actual data communication phase. This results in more reliable links per hop and ensures link budget above the receiver sensitivity threshold. This however creates more hops and a longer routing path in order to reach destination. Hence, RECLAIM further applies efficient and non-conflicting 6TiSCH scheduling method to coordinate those communication events in a non conflicting manner which do not collide or cause interference to each other, resolving the issue that has traditionally prevented this approach. Our detailed simulation shows that RECLAIM drops 60 times less packets than standard RPL, and achieves 99.9999% packet delivery ratio.

We propose practical sets of rules for adapting Clear Channel Assessment (CCA) and Transmit Power (TP) parameters by simulating ensembles of randomly generated wireless networks and collecting throughput statistics. The rules' performances depend strongly on network topology, with increases in throughput in many cases. However, networks with a high clustering coefficient are often adversely effected by the adaptations. But simulations of small-scale networks show that apparently adverse adaptations may still yield benefits for uneven demand combinations. Finally, we have found that throughput is not usually correlated with the number of hidden or exposed nodes in any non-trivial network set-up.

The term Tactile Internet broadly refers to a communication network that is capable of delivering real-time control, touch, and sensing/actuation information through sufficiently reliable, responsive, and intelligent connectivity. Envisioned to enable unprecedented applications, the Tactile Internet provides a promising opportunity to reshape industrial communication and transform the operation of many existing industrial systems. This paper investigates the role of Tactile Internet in current and future industrial systems and reviews the technological trends from legacy industrial networks to emerging industrial wireless networks, and beyond. To this end, this paper begins with an overview of the Tactile Internet. Complementing prior efforts, it presents a taxonomy of the key Tactile Internet applications. This paper covers the relevant background on the emergence of industrial communication. Then, it discusses the role of the Tactile Internet for various industrial systems and identifies the key use cases with respective connectivity requirements. This paper provides a technology landscape for the Tactile Internet to enable high-performance industrial wireless communication. Specifically, it provides insights into the recent Third Generation Partnership Project developments for enabling ultrareliable and low-latency communications over 5G mobile/cellular networks. It also examines the potential of the IEEE 802.11 and IEEE 802.15 standards for industrial control applications. Besides, it reviews the role of artificial intelligence and edge-computing platforms in overcoming the imperfections of wireless environments. Finally, this paper provides a roadmap for future, investigating the role of Tactile Internet in next generation industrial systems along with some directions and challenges for future research.

The Time Synchronised Channel Hopping (TSCH) MAC, adopted in popular WirelessHART, ISA100.11a and IEEE802.15.4e, is emerging as the de-facto technique for critical wireless industrial monitoring and control applications. However, much of its benefits are tied to the ability of the protocols and the scheduling techniques to optimally route traffic and orchestrate communication over individual links in a non- conflicting manner. Existing routing and scheduling approaches employ complex algorithms requiring excessive signalling overhead. While some of them can build optimal solutions under static conditions, they lack the agility to adapt to changes in network topology, traffic, and link qualities. To overcome these limitations, we propose BOOST, a protocol that blends TSCH with opportunistic routing and an effortless scheduling technique. BOOST complements the native support of spatial and channel diversity of the former with receiver diversity of the latter, pushing the envelope of network reliability to over 99.99%. Its ultra-lightweight autonomous scheduling mechanism adapts the communication schedule to network changes instantly with almost zero signalling overhead when other solutions struggle with tens of seconds or even minutes of delay with low reliability during this period.

The need for low power, long range and low cost connectivity to meet the requirements of IoT applications has led to the emergence of Low Power Wide Area (LPWA) networking technologies. The promise of these technologies to wirelessly connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out 'what-if analyses' and predeployment studies are needed to understand the implications of choices that are made at design time. While there are several promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts.

Recent studies show that a key contributor to congestion and increased CO2 emissions within cities are drivers searching (or cruising) to find a vacant on-street parking space. It has been shown that approximately (depending on the city) 20-30% of vehicles in congested urban areas were cruising to find a parking space with a parking search time varying in the order of several minutes. In the city of Bristol alone, we have shown, using our collected trip and publicly available census data that over 790 metric tons of CO2 is generated every year due to cruising. At a total cost of £368, 000 (US$467, 000) in terms of fuel wasted. The solution, described in this paper, aims to reduce parking search times using our automated real-time parking system called ParkUs 2.0. Our proposed method leverages sensor and location data collected from smartphones (carried by drivers), uses machine learning (classification) to detect cruising behaviour, automatically annotates parking availability on road segments based on the classified data and displays this information as a heatmap of parking availability information on the user's smartphone. This is the first such attempt to automatically detect cruising to the best of our knowledge. Evaluation through controlled trials with volunteer participants highlights the potential of our novel approach as we are able to detect cruising with an accuracy of 81%.

This paper proposes a dynamic user association method to address the load balancing problem in dense IEEE 802.11ax networks with uneven user distribution, where a user determines which AP to connect to taking into consideration of multiple factors such as RSS, potential relative capacity, achievable data rate and location of users. The method is user-centric and does not incur much signaling overhead, while performance optimization can be achieved without inter-AP coordination. Simulation results have been presented to show that the proposed solution can improve load balancing and have higher average throughput for 10% worst users as well as maintain the maximal total system throughput. Our evaluation also suggests that the location-awareness of users considered in the proposed solution plays an important role to improve the performance.

Time-Slotted Channel Hopping (TSCH) is a part of an emerging IEEE 802.15.4e standard to enable deterministic low-power mesh networking. It promises to pave the way to the future Internet of (Important) things by offering high reliability and low latency for wireless industrial applications. Nonetheless, the standard only provides a framework but it does not mandate a specific scheduling mechanism. In this paper, we propose a centralized Adaptive MUlti-hop Scheduling method (AMUS) based on the latest TSCH MAC. AMUS first enables sequential multi-hop scheduling to provide low latency guarantee for time-critical applications. Secondly, the novel tentative cell allocation method allocates additional resources to vulnerable links such that possible MAC retransmissions can be accommodated within the same slotframe, hence significantly reducing the delay caused by interference or collisions. Last but not least, the battery power of the node can be further conserved by adopting the proposed End-of-Q notification mechanism. Preliminary simulation results have confirmed that AMUS outperforms other popular scheduling algorithms in the literature.

Inter-cell interference mitigation techniques are playing important roles to improve the system performance, especially for dense network. Among them, the coordinated transmission has been used to tackle the interference problem. In this paper, we investigated the performances of coordinated transmission with stochastic network modeling and derived its expression on coverage probability and average rate with different number of base station coordination. The numerical results have been presented to compare the performance with frequency reuse technique. The results suggest that the coordinated transmission can perform better than frequency reuse in terms of coverage probability and average rate depending on the scheduling of the transmission of coordinated base stations.

With the advent of Machine to Machine (M2M) communication, we are witnessing an increased interest towards technologies that will enable efficient and reliable operation of Wireless Sensor Networks (WSN). Such networks are expected to include a large number of sensor devices which will generate large body of M2M traffic. To reduce the impact of this M2M traffic, efficient storage and retrieval methods should be employed in a distributed manner for the successful deployment of this technology. In this paper, a distributed storage solution is presented with the aim of reducing the impact of M2M traffic on data centres and the network backbone. The reliable and efficient storage of the sensor data is established by taking advantage of codes with Maximum Distance Separable (MDS) properties. The solution is implanted in Contiki OS using RPL protocol [1] and its performance is evaluated through simulations. Furthermore, to realise such a system, a centralised clique finding algorithm is demonstrated and benchmarked against a solution that uses a brute force approach.

This paper presents experimental results which support our proposition that the increase in bandwidth efficiency achievable by dynamically and transparently matching transport protocols and messaging patterns to application requirements outweighs the necessary increase in complexity and overhead of the proposed architecture. In order to validate this claim, DIRECTOR, a middleware solution for managing Smart Grid applications communication requirements has been developed. The results show that distributed systems, such as the Smart Grid data network, can benefit from the scalability and transport layer flexibility provided by the proposed middleware solution by being able to rapidly negotiate transport level communication options at run time. This will allow applications to operate with increased network efficiency and better meet the Quality of Service requirements specified by the applications. The middleware solution will abstract these additional complexities from the applications and provide a simple communications interface which requires no network infrastructure modifications to be supported. The fundamental components of the middleware solution are demonstrated through the use of a real time implementation of the Open Automated Demand Response (OpenADR) Real Time Pricing (RTP) data model. The experimental test bed includes both real hardware nodes (Raspberry Pi Model B) and virtualised nodes residing in a custom network emulator.

In low-power and lossy wireless networks, message delivery could be jeopardized by factors such as channel conditions, noise, interference etc. This can lead to communication retransmissions, resulting in significant energy depletion and consequently consumes limited on-node energy resources. This paper elaborates on a two-pronged approach to improve energy efficiency and reliability of communications: a) the application of content-centric data aggregation (pre-processing of correlated information) at each node to reduce traffic volume, and b) the use of link quality information to estimate local network lifetime while routing traffic. A novel objective function is proposed which ties the two together and findings from a performance evaluation are presented. It is shown that the proposed mechanism is able to achieve significant energy savings and prolongs the network lifetime in energy constrained multi-hop networks with lossy wireless links.

Selfishness and altruism are two distinctive node behaviors in wireless networks. Users of network nodes, e.g. mobile phones, would like their individual devices to be operational for as long as possible. On the other hand, users and operators of wireless networks may wish for an overall long network lifetime via collaborative node operations. In contrast to the most of the work done till now, in this paper, we consider a node-centric scenario, where a node can balance its energy consumption according to its needs to prolong the node lifetime. More specifically, the energy balancing problem is defined as an inventory control problem, where the node may choose to be selfish or unselfish with various degrees having in mind a range of parameters, such as its battery state and the existence and rate of energy harvesting. Each node can use the proposed solution to allocate the available energy between internal (node itself) and external (collaborative) tasks subject to dependencies among application and communication layers and the node operating system. We also discuss a number of other possible extensions of the method to include interactions among different nodes in the network. To illustrate one such an extension, a case study with a large number of nodes among two separate but co-located wireless mesh networks is presented. By applying the inventory balancing algorithm, considerable increase in the network lifetime is shown for both networks.

In this paper, a new spectrum allocation method is proposed to address the downlink interference problem in heterogeneous macro-femto networks, where distributed femtocells coexist with macrocells. In practical heterogeneous macro-femto networks, normally there is no direct link between a macro base station and a femto base station, and any communication between the two has to be via the backhaul and hence delay-tolerant. Meanwhile, due to wide deployment of distributed femtocells, the signaling overhead via the backhaul should be kept as low as possible. To this end, this paper comes up with a dynamic fractional frequency reuse and dynamic resource allocation scheme to tackle the interference problem, taking into consideration the delay- tolerance and low overhead requirements. Performance evaluation results have demonstrated the benefits of the proposed scheme.

Transporting raw data which is usually correlated over multi-hop wireless links can be costly both in terms of time as well as resources. This is likely to be a pressing problem particularly in the emerging Machine-to-Machine (M2M) communication paradigm wherein the amount of data is expected to proliferate. With in-network processing and data aggregation, the total amount of forwarding traffic on multi-hop routes can be significantly reduced by pre-processing of correlated information. However, a suitable data forwarding scheme is needed as a prior condition in order to efficiently compute and relay data. By taking advantage of distributed processing, this paper provides a content centric and load balancing aware distributed data routing solution for large-scale multi-hop M2M wireless networks. Independent routing decisions are made by each node to refine its next hop data forwarding node selection using only local information. Hence, it is highly adaptive to dynamic environments. A hybrid objective function is proposed for route selection which includes two main parts: 1) reduce the communication traffic by aggregating similar type of data, hence increasing the processing gain; 2) balance the energy-consumption among neighbouring nodes taking into account heterogeneous node residual energy levels so as to reduce both computation and communication costs and at the same time avoid early energy depletion of hot-spot nodes. We show that the proposed algorithm is able to extend the network lifetime and provides significant energy saving in complex large-scale Multihop wireless networks.

Radio Environment Map (REM) is an integrated database consisting of multi-domain information including the spectrum availability profile, geographical features, regulations, relevant policies, radio equipment profile, past experiences, etc. In this paper we analyse the integration of REM with the wider communication system and how it interfaces with the relevant network entities. REM is primarily designed to facilitate network establishment and to improve operational performance and radio resource management. The REM computes complex temporal and spatial aspects of radio environment by gathering information from various spectrum sensing sources, and thus has interface with heterogeneous devices. In this paper we particularly look at the different interfaces of REM with other potential entities in the architecture. The interface design must take into consideration the stakeholders providing REM facilities and the architecture in which the functions are mapped. These are necessarily scenario specific as they depend on the motivation, benefits and incentives for using the REM approach. For selected use case scenarios, we draw architectural interface design considerations and network mapping.

One of the fundamental challenges in wireless ad-hoc networking is promotion of cooperation among rational and selfish nodes. Various schemes have been proposed in the literature to stimulate nodes collaboration by improving data forwarding function or route discovery function. The novel algorithm proposed in this paper enhances these two ad-hoc networking functions by utilizing a unique credit-reputation hybrid approach. In the data forwarding phase, a reputation and credit combined mechanism offers incentives with simpler and less overhead process; in the route discovery phase, a new routing metric considering the remaining energy as well as balance between credits and energy has been introduced. Simulations results show that the scheme could successfully stimulate node cooperation by manipulating credits and reputation restrictions and more importantly actively discouraging three types of selfish behaviors.

In cognitive radio networks, the statistical duty cycles (traffic loads) of primary channels have a decisive effect on the throughput and link maintenance of secondary users (SUs). It is very difficult and time-consuming for the SUs to distinguish between primary channels in terms of duty cycles. This paper proposes a multi-user cooperation scheme where SUs share status information about the primary channels and the corresponding cooperation information processing scheme based on Bayesian inference to differentiate the primary channels in terms of statistical duty cycles within a short period of time. The cooperation information processing scheme helps the SUs quickly make accurate estimates of the statistical duty cycles of primary channels and theire uncertainties. Specifically, with the aid and information from its cooperating neighbors, a SU will continuously update the likelihood function and the priori distribution for each primary channel. Analytical and simulation results show that the proposed multi-user cooperation scheme, which is based on a Bayesian updating rule, can significantly save a SU's time in channel detection and selection, and therefore greatly improve its data transmission time and throughput.

Responsive transport protocols e.g. TCP, back-off on packet loss. This is because packet loss is by default treated as a sign of congestion. However losses in a wireless network may not only arise from congestion but also because of the inherent characteristics of the wireless medium. If a TCP source assumes a wireless loss as a loss due to congestion and reduces its transmission rate, it fails to take advantage of the available capacity in the network thereby attaining less throughput than it would have achieved otherwise. This problem can be solved by making the TCP source intelligent so that, on detecting a packet loss, it will be able to distinguish what type of loss it is (a congestion loss or a wireless loss) and react accordingly - halve its transmission rate in the former case thereby helping to relieve congestion and maintain the status quo in the latter thereby helping to achieve better throughput at the same time remaining TCP friendly. This paper proposes a new algorithm called TCP E2E to achieve this. TCP E2E is a modified version of TCP Reno which operates entirely at the sender side, does not require support from any other node in the network and helps to achieve better throughput compared to TCP Reno under certain conditions as observed through a simulation based study carried out using the ns2 network simulator. Moreover, TCP E2E is TCP friendly thereby enabling coexistence with legacy transport protocols.

In this survey paper, we investigate some of the challenges to be addressed before a practical real-time secondary market for spectrum can be developed. We differentiate the methods of dynamic spectrum allocation as coordinated usage of resources, interworking solutions, integration solutions and secondary access of spectrum. Secondary spectrum access approaches are generally classified as real-time and non-real-time secondary access. However, the focus of this work is on real-time secondary spectrum utilization which can follow a negotiated or opportunistic access strategy. While different solutions for increasing spectrum utilization and efficiency are under investigation, many aspects of spectrum sharing technologies are still open questions. After an extensive literature survey, the major challenges and open questions of real-time secondary usage of spectrum are addressed here. We also provide some insights on potentially important design considerations and requirements when developing successful spectrum management schemes to realize real-time secondary spectrum usage.

There is growing interest within industry and standardisation bodies to permit wireless local area networks to utilise the scarce radio resources more efficiently and intelligently. One approach is to increase the physical layer data rate and exploit multi-path and spatial diversity using multi antenna systems, the other, and complementary, approach is to use more intelligent dynamic channel and mode selection. Both approaches are inter-related, but in this paper we focus on techniques for the latter. In particular we address the dynamic channel and mode selection problem from a terminal point of view and have implemented a solution that provides significant performance enhancements over conventional techniques when applied to existing wireless local area networks.