Resource Dependency and Multi-tenancy in Mobile Edge Clouds (MEC) present major resource orchestration challenges owing to limited and interdependent resources and dynamic deployment environments. Traditional cloud computing solutions are resource-dependency-agnostic, failing to maintain fair resource allocation and achieve high Service Level Objectives (SLOs) rates. In this paper, we present EdgeCore, a Resource Dependency (RD)-aware multi-tenant orchestrator for MEC. EdgeCore includes three integral components: 1) RD Resolver, which resolves the RD problem ignored by prior orchestrators; 2) EdgeDRF establishes multi-resource fairness among multiple tenants in MEC where prior solutions fall short; 3) EdgePilot utilizes the Resource Monitor, Resource Recommender, and Screening Algorithm, to continuously maintain the fair resource allocation across various deployments. EdgeCore is the first resource dependency-aware orchestrator outperforming the state-of-the-art AutoPilot and H-DRF with 20% higher SLO satisfaction rates evaluated in disaster response scenarios. Its lightweight implementation can be generalized to a multitude of MEC environments. EdgeCore code is available at https://github.com/LENSS/EdgeCore-SEC2024.

Mobile Edge Computing (MEC) has been gaining a major interest for use in Cyber-Physical Systems (CPS) for Disaster Response and Tactical applications. These CPS generate a very large amount of mission-critical and personal data that require resilient and secure storage and sharing. In this article, we present the design, implementation, and evaluation of a framework for resilient data storage and sharing for MEC in CPS targeting the aforementioned applications. Our framework is built on the resiliency of three main components: EdgeKeeper, which ensures resilient coordination of the framework’s components; RSock, which provides resilient communication among CPS’s nodes; and R-Drive/R-Share which, leveraging EdgeKeeper and RSock, provides resilient data storage and sharing. EdgeKeeper employs a set of replicas and a consensus protocol for storing critical meta-data and ensuring fast reorganization of the CPS; RSock decides an optimal degree for replicating data that is communicated over lossy links. R-Drive employs an adaptive erasure-coded and encrypted resilient data storage; R-Share, leveraging RSock provides resilient peer-to-peer data sharing. We implemented our proposed framework on rapidly deployable systems (e.g., manpacks, testMobile Edge Clouds) and on Android devices, and integrated it with existing MEC applications. Performance evaluation results from three real-world deployments show that our framework provides resilient data storage and sharing in MEC for CPS.

This poster presents a multi-access network testbed designed to serve as an essential tool for research on Access Traffic Steering, Switching, and Splitting (ATSSS) in 4G and 5G networks. By integrating Non-3GPP Inter-Working Function (N3IWF) and 5G core from different open-source projects, our testbed enables multiaccess sessions in 4G-LTE, 5G NSA, and 5G SA. Furthermore, our testbed incorporates a transport converter into the 5G core network, allowing User Equipment (UE) to utilize multi-access networks, even in the absence of multi-path TCP (MPTCP) support from external servers. We demonstrated the effectiveness of our testbed by utilizing ATSSS for detecting cybersecurity attacks, marking the first instance of employing ATSSS capabilities to improve cybersecurity in 4G/5G networks.

Accurate and prompt delivery of Emergency Medical Services (EMS) is critical in emergency incidents, e.g., man-made or natural disaster areas. However, quickly selecting the correct EMS protocol(s) (which dictate the medical procedures to be administered to patients) in complex medical scenarios, remains a key, demanding task for Emergency Medical Technicians (EMT). In this paper, we present EMSAssist, the first end-to-end mobile voice assistant at the edge for EMS. EMSAssist consists of three major components that address technical challenges present in state-of-the-art solutions: 1) For the first time, EMSAssist proposes and applies a few-sample fine-tuning technique in medical speech recognition task, that achieves a faster and more accurate speech transcription on our EMS audio dataset, when compared to Google Cloud Speech-to-Text; 2) A WordPiece tokenizer helps boosting the end-to-end EMS protocol selection accuracy by retrieving useful information from incorrect transcriptions; 3) A novel data customization framework that enables our data-driven EMSMobileBERT model to become the new state-of-the-art for EMS protocol selection. Extensive end-to-end evaluation results at the edge show EMSAssist can more accurately select EMS protocols (Top-5 accuracy above 96%) for EMTs, with end-to-end latencies of around 4.2 seconds.

We present EMSAssist, the first end-to-end mobile voice assistant for emergency medical services (EMS). EMSAssist allows Emergency Medical Technicians (EMT) to verbally describe patients' signs and symptoms and uses EMTs' voice input to recommend top-5 EMS protocols. Through this demo, we allow the attendees to evaluate EMSAssist through a pair of Google Glass and a mobile phone. Both mobile devices can collect users' voices as input and output top-5 recommended protocols. A companion youtube video of using EMSAssist on the Google Glass is provided: https://www.youtube.com/watch?v=bj7aQJKf4aE

In this paper, we show that Hybrid Routing and Prophet protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to theCollusiveHijackattack, in which a malicious attacker, Eve, compromises a set of nodes and lies about their Inter-Contact-Times (ICTs). Eve claims that her nodes meet more frequently than in reality to hijack the routes of legitimate nodes in OMNs. The CollusiveHijack attack enables Eve to launch more severe attacks like packet modification, traffic analysis, and incentive seeking attacks. To identify the CollusiveHijack attack, we propose the Kolmogorov-Smirnov two-sample test to determine whether the statistical distribution of the packets’ delays follows the derived distribution from the ICTs among the nodes. We propose three techniques to detect the CollusiveHijack attack, the Path Detection Technique (PDT), the Hop Detection Technique (HDT), and the Early Hop Detection Technique (EHDT), which trade off compatibility with the Bundle Security Protocol, the detection rate, and the detection latency. We evaluated our techniques through extensive trace-driven simulations and a proof-of-concept system implementation and show that they can detect CollusiveHijack attacks with 80.0% to 99.4% detection rates (when Eve hijacks more than 60 packets) while maintaining a low false positive rate ($\sim$3.6%) and a short detection latency (7-14 hours) for EHDT (75%-85% enhancement compared to PDT and HDT).

Mobile Edge Computing (MEC) is gaining significant interest from first responders and tactical teams. Typical cloud-based coordination (e.g., service discovery and coordination, device naming, authentication) does not work in MEC due to high user mobility. We design and implement EdgeKeeper to provide cloud-like service coordination to distributed edge computing applications for MEC systems. It maintains an edge cluster among devices and intelligently stores data on a group of replicas to guard against node failures/disconnections. We provide a full-system implementation of Edge-Keeper for Android and Linux platforms and evaluate it with MEC applications in a real-world wide-area search and rescue operation conducted by first responders.

The popularity of video cameras has spawned a new type of application called multitask video processing, which uses multiple CNNs to obtain different information of interests from a raw video stream. Unfortunately, the huge resource requirements of CNNs make the concurrent execution of multiple CNNs on a single resource-constrained mobile device challenging. Existing solutions solve this challenge by offloading CNN models to the cloud or edge server, compressing CNN models to fit the mobile device, or sharing some common parts of multiple CNN models. Most of these solutions, however, use the above offloading, compression or sharing strategies in a separate manner, which fail to adapt to the complex edge computing scenario well. In this paper, to solve the above limitation, we propose AMVP, an adaptive execution framework for CNN-based multitask video processing, which elegantly integrates the strategies of CNN layer sharing, feature compression, and model offloading. First, AMVP reduces the total computation workload of multiple CNN inference by sharing some common frozen CNN layers. Second, AMVP supports distributed CNN inference by splitting big CNNs into smaller components running on different devices. Third, AMVP leverages a quantization-based feature compression mechanism to reduce the feature transmission traffic size between two separate CNN components. We conduct extensive experiments on AMVP and the experimental results show that our AMVP framework can adapt to different performance goals and execution environments. Compared to two baseline approaches that only share or offload CNN layers, AMVP achieves up to 61% lower latency and 10% higher throughput with comparative accuracy.

The new generations of mobile devices have high processing power and storage, but they lag behind in terms of software systems for big data storage and processing. Hadoop is a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware. Building Hadoop on a mobile network enables the devices to run data intensive computing applications without direct knowledge of underlying distributed systems complexities. However, these applications have severe energy and reliability constraints (e.g., caused by unexpected device failures or topology changes in a dynamic network). As mobile devices are more susceptible to unauthorized access, when compared to traditional servers, security is also a concern for sensitive data. Hence, it is paramount to consider reliability, energy efficiency and security for such applications. The MDFS (Mobile Distributed File System) [1] addresses these issues for big data processing in mobile clouds. We have developed the Hadoop MapReduce framework over MDFS and have studied its performance by varying input workloads in a real heterogeneous mobile cluster. Our evaluation shows that the implementation addresses all constraints in processing large amounts of data in mobile clouds. Thus, our system is a viable solution to meet the growing demands of data processing in a mobile environment.

In this paper, we first show that Group Owner (GO) devices in Wi-Fi Direct are vulnerable to the EvilDirect attack. In the EvilDirect attack, a rogue GO is set up by an adversary to look like the legitimate GO (with the same MAC address, SSID, and operating channel). The adversary intercepts the clients' invitation requests and accepts them before the legitimate GO. Accordingly, the adversary hijacks the wireless communications between the clients and the legitimate GO. To defend against the EvilDirect attack, we propose the idea of exploiting the received signal strength (RSS) variations on the wireless channel between each client and the legitimate GO. Our solution, EvilDirectHunter checks whether the RSS profiles of both the client and the potential GO devices are similar with each other. Both devices incrementally prove this similarity by exchanging challenge and response packets. EvilDirectHunter is evaluated by implementing it as an Android App, and by modifying the Android kernel code responsible for Wi- Fi Direct in Google Nexus 5 and Samsung Galaxy S2 smartphones. The results show that EvilDirectHunter is able, within seconds, to identify EvilDirect attacks with a high detection rate (100%) while maintaining a low false positive rate (4.5%).

Today's integration of mobile technologies and traditional cloud computing exploits the abundant computation and storage resources in the cloud, to enhance the capabilities of end-user mobile devices. The designs that rely on remote cloud services, however, sometimes overlook the abundant resources (e.g., storage, communication, and computation) on mobile devices. In particular, when the remote cloud services are unavailable (due to service downtime or network issues), these smart devices can no longer function. We propose a Heterogeneous Mobile Cloud (HMC) computing design that efficiently utilizes the communication and computation resources to support data storage and data processing services in a group of mobile devices. Each mobile device may have different energy, communication and computation capabilities, but our Mobile Storage & Processing System (MSPS) ensures that: i) the communication and computation tasks are executed in an energy-efficient manner, ii) task allocation considers device heterogeneity and achieves system-wide load balancing, and iii) the stored data are fault-tolerant. Through extensive simulations and real hardware implementations on Android devices, we demonstrate the performance and feasibility of deploying MSPS in a real heterogeneous mobile environment.

Energy efficiency is of utmost importance for wireless networks due to the prevalent usage of mobile devices in WLAN and wireless sensor networks (WSNs). Consequently, duty cycling MAC protocols are widely employed in both WLAN (i.e., power saving mode - PSM) and WSNs (i.e., low power listening - LPL)). In a coexisting PSM and LPL network, a significant energy efficiency degradation has been observed because of large deviations of MAC protocol parameters from optimal values. To investigate these inefficiencies, analytical models are greatly preferred, when compared to simulation based methods, since they are faster, more scalable and can be used in optimization problems. Thus, this paper presents the first model for throughput and energy consumption analysis of coexisting PSM and LPL. Moreover, this paper presents a performance tuning method that minimizes the total energy consumption while satisfying the throughput requirements. Through extensive simulations, we demonstrate the accuracy of our proposed models and analysis. Finally, the proposed tuning method is evaluated, proving its effectiveness.

Neighbor discovery is a crucial building block required by important protocols in wireless ad hoc and sensor networks. Routing protocols usually rely on exchanging messages between neighboring nodes and many localization protocols determine nodes' locations based on their neighborhood relationships to other nodes or anchors. In hostile environments an adversary may launch subtle attacks against neighbor discovery by relaying neighbor discovery messages between far away areas in the network to make distant nodes believe to be neighbors. Relay attacks, also known as wormhole attacks, can not be prevented using cryptography because encrypted messages can also be relayed by the adversary. In this paper, we propose an efficient approach to detect and localize wormhole attacks by embedding the network graph in a higher dimensionality space, to identify the inconsistencies among the ranging information as the inherent effect of such attacks. We show the effectiveness of our approach in terms of accuracy and not being sensitive to ranging error, by performing simulations.

Real world wireless networks usually have diverse connectivity characteristics. Although existing works have identified replication as the key to the successful design of routing protocols for these networks, the questions of when the replication should be used, by how much, and how to distribute packet copies are still not satisfactorily answered.

Coexisting 802.11 and 802.15.4 single-cell wireless networks are experiencing significant performance degradation due to the aggressive nature of 802.11 (when compared to 802.15.4) and to their different traffic characteristics [1]. Providing tight delay guarantees to certain 802.15.4 applications (e.g., health monitoring with unsaturated, periodic traffic) is becoming increasingly infeasible, in the presence of coexisting WiFi with bursty and bandwidth-hungry traffic. Optimizing the performance of these coexisting networks (e.g., WiFi throughput maximization, while satisfying 802.15.4 deadlines) has been a challenging task, primarily due to the lack of: i) analytical models that take into consideration realistic network traffic conditions; and ii) accurate simulators for coexistence. In this paper, we address the aforementioned research challenges by modeling the transmission buffers of wireless devices as M/G/1 queues, and employ queuing theory and Markov Chain models to derive, for the first time, closed form solutions for throughput and delay in 802.11/802.15.4 coexisting networks. Using our proposed models, this paper presents a novel approach for joint MAC protocol tuning, that maximizes 802.11 throughput while satisfying delay constraints of 802.15.4. We validate our proposed solutions and models through new 802.11/802.15.4 coexistence capabilities in the ns-3 simulator (important for the research community).

In this paper we first show that the Wi-Fi Protected Setup (WPS) protocol (used by Wi-Fi Direct, the de facto adhoc communication mechanism for smartphones and mobile devices) is vulnerable to a brute-force or dictionary attack. To defend against these attacks, we propose the idea of using contextual information (i.e., data obtained from mobile device's sensors) to establish a long (128 bits) secure session key between two Wi-Fi Direct enabled devices, instead of using the keypad. Our solution, Session Key Generated from Sensors (SekGens) employs three phases. In the Quantization Phase, the key is iteratively generated based on different sensors' data. In the Reconciliation Phase, the two devices eliminate minor differences in the bits of their keys by using the Cascade reconciliation mechanism. In the Privacy-Amplification-and-Hashing Phase, the two devices omit all bits exposed during the reconciliation phase and apply hashing to the remaining secret bits. SekGens is implemented and evaluated by modifying the Android kernel code responsible for WPS in Google Nexus 5 and Samsung Galaxy S2 smartphones. The results show that SekGens generates keys with low mismatch ratio (less than 3%), at a fast rate (~20 bits/sec), and with high entropy (~92%).

Routing protocols for opportunistic social network (OSN) utilize popular nodes to achieve good routing performance with low overhead. This, however, may result in a severe energy consumption imbalance. The existence of Transient Connected Components (TCC) further complicates the problem due to the randomness of TCC's topology. This paper investigates the energy consumption imbalance problem in OSNs with TCCs. We show that TCC-aware routing protocols, despite their superiority in routing performance, suffer from a more severe imbalance problem. We propose an Energy Consumption Balanced Routing protocol, which includes a new metric and a routing protocol independent mechanism. We analytically show that our protocol leads to the convergence of aggregate traffic carried by each node. Through simulation on real world traces, we show that our protocol reduces the energy imbalance by up to 31%, while maintaining comparable routing performance, and that our protocol independent mechanism balances the energy consumption of existing OSN routing protocols.

Energy efficiency is a significant aspect in wireless LANs due to the pervasive usage of mobile devices. The most effective way to save energy is turning off the radio, which is the key feature of the IEEE 802.11 power saving mode (PSM) protocol. Understanding the performance such as throughput and energy consumption of 802.11 PSM networks is important because they depend highly on the parameters we choose for contention window size, duty cycling ratio, etc. For this purpose, an analytical model is greatly preferred comparing to the tedious simulation because the former is fast, scalable and can be easily used to optimize performance. This paper presents a comprehensive analytical model for 802.11 IBSS PSM. We derive analytically, using Markov chains, the throughput, total delay and energy consumption, simulate the network using ns-3 simulator, and validate our results through extensive simulations. This model can be conveniently applied to accurately estimate and optimize the performance of single-cell 802.11 PSM ad-hoc networks.

An Ad-hoc Mobile Cloud (AMC) is a new computing model that allows sharing computing power of multiple mobile devices. For a diverse group of individuals that employ such computing model, in an ad-hoc manner, secure peer-to-peer communication becomes very important. Using private or pairwise keys to secure such communication is preferable to public-keys because of computation and energy requirements [1]. With the advent of sensor enabled mobile devices, a protocol (SekGens) that uses sensor data to generate pairwise keys on demand has been proposed [2]. To work successfully SekGens requires devices to be closely located and becomes infeasible for devices situated multiple hops away. SekGens is also expensive in computation and slow in key generation. In this paper, we investigate how to enable devices in an AMC to establish pairwise keys. We propose an efficient solution which tries to reduce the number of executions of SekGens in the AMC, and establishes pairwise keys between nodes multiple hops away by distributing parts of the key on multiple routing paths. Our results show a reduction of up to 75% in the number of SekGens required to establish keys in an AMC, when compared to a naive approach.

In this article, we present RE2MR, the first hybrid multicast routing protocol that builds on the strengths of existing topology-based, hierarchical and geographic multicast solutions, while addressing their limitations. In RE2MR, the multicast path search problem is formulated as the capacitated concentrator location problem (CCLP) which yields the network topology that minimizes the sum of path lengths from the multicast root to multicast members. Furthermore, its trajectory-based lightweight hole detection (THLD) discovers deployment area irregularities (i.e., network holes) that affect its solution and autonomously take them into account to generate updated routing paths, and its Energy-efficient Packet Forwarding (EPF) and Multi-level Facility Computation (MFC) reduce computational and communication overheads. We implement RE2MR in TinyOS and evaluate it extensively using TOSSIM for relatively large-scale simulations (400 nodes); we also implement RE2MR on real-hardware and perform experiments on a testbed consisting of 42 TelosB motes. Through the simulations and experiments on real-hardware, we demonstrate that RE2MR reduces the energy consumption by up to 57 percent and the end-to-end delay by up to 8 percent, when compared with the state-of-the-art multicast routing protocols.

Despite the advances in hardware for hand-held mobile devices, resource-intensive applications (e.g., video and image storage and processing or map-reduce type) still remain off bounds since they require large computation and storage capabilities. Recent research has attempted to address these issues by employing remote servers, such as clouds and peer mobile devices. For mobile devices deployed in dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility as in a mobile cloud), however, challenges of reliability and energy efficiency remain largely unaddressed. To the best of our knowledge, we are the first to address these challenges in an integrated manner for both data storage and processing in mobile cloud, an approach we call k-out-of-n computing. In our solution, mobile devices successfully retrieve or process data, in the most energy-efficient way, as long as k out of n remote servers are accessible. Through a real system implementation we prove the feasibility of our approach. Extensive simulations demonstrate the fault tolerance and energy efficiency performance of our framework in larger scale networks.

Monitoring flow-based systems (FBS) (e.g., water distribution systems, oil and gas pipelines, the human cardiovascular system) is of paramount importance considering their economic and health impacts. FBS monitoring typically has been achieved by costly, complex, static sensors that are strategically placed. To reduce the cost of monitoring, we propose a mobile wireless sensor network (WSN) system comprised of mobile sensors (their movement aided by the inherent flow in the FBS) and static beacons that aid in locating sensors. This article presents the first complete architectural design, algorithms, and protocols for optimal monitoring of FBS. Our proposed solution includes sensing and communication models, MAC and group management protocols for sensor and beacon communication, and algorithms for sensor and beacon placement. We compare our proposed solution with the state of the art through extensive simulations and a proof-of-concept system implementation. We demonstrate performance improvements, such as a dramatic reduction (a factor of 91) in the number of sensors when the sensing range is marginally (2.5 times) increased.

Leak and backflow detection are essential aspects of Water Distribution System (WDS) monitoring. Most existing solutions for leak/backflow detection in WDSs focus on the placement of expensive static sensors located strategically. In contrast to these, we propose a solution whereby mobile sensors (i.e., their movement aided only by the inherent water flow in the system) detect leaks/backflow. Information about the leaks/backflow is collected from the sensors either by physically capturing them, or through wireless communication. Specifically, we propose models to maximize leak/backflow detection given a cost constraint (a limit on the number of sensors). Through extensive simulations, we demonstrate the superior performance of our proposed solution when compared with the state of the art solutions (e.g., algorithms/protocols and analysis).

Water Distribution Systems (WDSs) are prone to events such as leaks, breaks, and chemical contamination. Continuous monitoring of WDSs for prompt response to such events is of paramount importance. WDS monitoring has been typically performed using static sensors that are strategically placed. These solutions are costly and imprecise [9] [18]. Recently mobile sensors for WDS monitoring has attracted research interest to overcome the shortcomings of static sensors [21] [14] [11]. However, most existing solutions are either unrealistic, or focus on on-demand methods (i.e., deploying sensors when presence of an event is suspected). In this paper, we propose a Cyber-Physical system (CPS) - CPWDS, for continuous monitoring of a WDS. Mobile sensors reside in the CPWDS and move with the flow of water in pipes; mobile sensors communicate with static beacons placed outside the pipes, and report sensed data; the flows in the pipes are controlled to prevent sensors from getting stuck and to ensure the sensors cover the main pipes of the WDS. We evaluate the proposed algorithms/protocols for the communication, computation and control of the CPWDS and demonstrate their performance through extensive simulations.

Social-based routing in opportunistic networks has been an active research topic in recent years, since utilizing social structure can improve routing performance significantly in networks where nodes are mobile devices carried by people. Inheriting from traditional delay tolerant networks routing, a node in social-based routing makes a routing decision based only on the peer it is currently in contact with. We argue that by taking into consideration only pairwise contacts and not considering multiple simultaneously existing links in opportunistic networks, inefficiencies in communication occur. In this paper, we first demonstrate the existence and stability of multi-links in opportunistic social networks, using real world and synthetic mobility traces. In order to address also demonstrated inefficiencies of existing social-based routing protocols, we propose a network coding based Contact Optimization algorithm which reduces the number of packet transmissions during contacts, while maintaining the same Packet Delivery Ratio (PDR) and Packet Delivery Delay (PDD). We perform simulations using real traces. The results show that our Contact Optimization algorithm is able to reduce the number of transmission by up to 20% while maintaining performance in terms of PDR, PDD when compared with Epidemic and Spray&Wait routing.

Geographic routing is well suited for large-scale wireless sensor networks (WSNs) because it is nearly stateless. One important challenge is that network holes may arbitrarily increase the routing path length. Fortunately, recent studies have shown that constant path stretch is achievable using nonlocal information. The constant stretch, however, is possible at the cost of high communication and storage overhead: a source node must complete a “path-setup” process prior to data transmission by exchanging a message with a destination node using a default geographic routing (e.g., GPSR). In this article, we propose the first geographic routing protocol (LVGR) that provably achieves worst-case stretch of Θ (D/γ) (where D is the diameter of the network and γ is the communication range of nodes) with low communication and storage overhead . LVGR represents a hole as a convex hull, the internal structure of which is represented as a local visibility graph . Based on the convex hulls and local visibility graphs, LVGR generates paths with guaranteed stretch. Through theoretical analysis and extensive simulations, we prove the worst-case stretch of LVGR and demonstrate that LVGR reduces communication overhead by up to 97% and storage overhead by up to 60%, compared with the state of the art.

Network simulation is an essential tool for the design and evaluation of wireless network protocols, and realistic channel modeling is essential for meaningful analysis. Recently, several network protocols have demonstrated substantial network performance improvements by exploiting the capture effect, but existing models of the capture effect are still not adequate for protocol simulation and analysis. Physical-level models that calculate the signal-to-interference-plus-noise ratio (SINR) for every incoming bit are too slow to be used for large-scale or long-term networking experiments, and link-level models such as those currently used by the NS2 simulator do not accurately predict protocol performance. In this article, we propose a new technique called the capture modeling algorithm (CAMA) that provides the simulation fidelity of physical-level models while achieving the simulation time of link-level models. We confirm the validity of CAMA through comparison with the empirical traces of the experiments conducted by various numbers of CC1000 and CC2420-based nodes in different scenarios. Our results indicate that CAMA can accurately predict the packet reception, corruption, and collision detection rates of real radios, while existing models currently used by the NS2 simulator produce substantial prediction error.

The explosion in the number of 802.11 and 802.15.4 deployments is exacerbating the coexistence problem, which has been reported in the literature to cause significant performance degradation in co-located networks employing the two different wireless standards. The wireless coexistence problem has, thus far, been studied primarily using hardware, due to the lack of analytical results and good wireless coexistence simulators. This paper presents the first analytical model for coexisting 802.11 and 802.15.4 networks. We derive analytically, using Markov chains, the normalized saturation throughput under coexistence. Additionally, we propose a performance tuning method that ensures QoS and a distributed Nash-equilibrium-based method that ensures fairness. We validate our model and the tuning methods using a coexistence simulator previously developed and presented by the authors. We demonstrate that our model has a low average error smaller than 10%.

Resource Allocation has been widely used for improving various performance metrics in wireless networks. Applying resource allocation to a Mobile Ad Hoc Network (MANET), however, is a challenging problem because of dynamic network topology. In this paper, we develop a novel resource allocation scheme designed for MANETs that minimizes the communication cost for accessing distributed resources while improving the reliability by adopting the k-out-of-n system, a widely used technique for reliability control. Specifically, we propose a scheme that allocates resources to n nodes, called service centers, such that the expected energy consumption for nodes to access k service centers out of the n service centers (k⩽n) is minimized. Our scheme accounts for dynamic network topology by estimating the failure probabilities of nodes and monitoring the network for significant topology changes. In addition, an Importance Sampling technique is used to reduce the computation-overhead. To evaluate the performance, we build a mobile distributed file system based on our resource allocation scheme. Through both extensive simulations and real hardware implementation on Smartphones, we show that our resource allocation scheme effectively reduces energy consumption by up to 45% and increases the successful data retrieval rate by up to 50% in comparison with a greedy algorithm.

The explosion in the number of WiFi and Wireless Sensor Network (WSN) deployments is exacerbating the coexistence problem, observed and reported in the literature as a significant performance degradation in co-located networks employing the two different wireless standards. The wireless coexistence problem has, thus far, been studied primarily using real hardware, due to the lack of analytical results and lack of good wireless coexistence models in network simulators. Thus, the progress on addressing the wireless coexistence issues has been slow. This paper presents the first analytical model and the first protocol coexistence simulator for coexisting WiFi and WSNs. We derive analytically, using Markov chains, the normalized saturated throughput of coexisting WiFi and WSNs, simulate the protocol coexistence using Monte Carlo methods, and validate our results through extensive experiments on real hardware (~4 million and ~60 million WSN and WiFi packets, respectively). These tools can be used for convenient and accurate performance estimation of medium-large scale deployments.

With the advance of mobile devices, cloud computing has enabled people to access data and computing resources without spatiotemporal constraints. A common assumption is that mobile devices are well connected to remote data centers and the data centers securely store and process data. However, for systems like mobile cloud deployed in infrastructureless dynamic networks (i.e., with frequent topology changes because of node failure/unavailability and mobility), reliability and energy efficiency remain largely unaddressed challenges. To address these issues, we develop the first 'k-out-of-n computing' framework that ensures nodes retrieve or process data stored in mobile cloud with minimum energy consumption as long as k out of n storage/processing nodes are accessible. We demonstrate the feasibility and performance of our framework through both hardware implementation and extensive simulations.

Disaster Response Networks (DRNs) are disruption tolerant networks designed to deliver mission critical data during disaster recovery, while operating with limited energy resources. While Quality of Service is desired, it is difficult to offer guarantees because of the unpredictable nature of mobility in such DRNs. The variance of the packet delivery delay (PDV, more commonly called jitter), an important QoS metric which in DRNs is measured in tens of minutes instead of milliseconds, has not been sufficiently addressed in recent research. Smartphones used by first responders generate large data workloads, causing the PDV to further degrade. Reducing packet replication at these workloads will lower energy consumption, but reduces the packet delivery ratio (PDR). The complex interplay between these QoS metrics remains unclear, making their control difficult. We present Raven, a routing protocol for DRNs that offers control over QoS, especially the PDV. Stochastic graph theory which deals with probabilistic edge weights having a mean and variance is used to model mobility in the disaster area. A stochastic version of the K-Shortest Paths algorithm routes data over multiple paths simultaneously. Raven has been thoroughly evaluated in simulation using realistic settings. The dynamics between performance and energy consumption is analyzed mathematically, and its control is demonstrated.

The node localization problem in mobile sensor networks has received significant attention. Recently, particle filters adapted from robotics have produced good localization accuracies in conventional settings. In spite of these successes, state-of-the-art solutions suffer significantly when used in challenging indoor and mobile environments characterized by a high degree of radio signal irregularity. New solutions are needed to address these challenges. We propose a fuzzy logic-based approach for mobile node localization in challenging environments. Localization is formulated as a fuzzy multilateration problem. For sparse networks with few available anchors, we propose a fuzzy grid-prediction scheme. The fuzzy logic-based localization scheme is implemented in a simulator and compared to state-of-the-art solutions. Extensive simulation results demonstrate improvements in the localization accuracy from 20 to 40 percent when the radio irregularity is high. A hardware implementation running on Epic motes and transported by iRobot mobile hosts confirms simulation results and extends them to the real world.

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.

Time is a critical factor in the Urban Search & Rescue operations immediately following natural and man-made disasters. Building on our collaboration with first responders we identify a set of areas for improving response times: victim detection in collapsed buildings, information storage and collection about buildings (collapsed or not), detection of first responder team separation and lost tools, and throughput and latency of data delivered to first responders. In this paper, we present the design (i.e., software/hardware architectures, and the guiding design principles), implementation and realistic evaluation of DistressNet, a system that targets the aforementioned areas for reducing the Urban Search & Rescue response time. DistressNet, built on COTS hardware and on open standards and protocols, pushes complexity that the very diverse Urban Search & Rescue scenarios pose, to user level applications (apps). Apps in DistressNet run on unmodified hardware ranging from smartphones, to motes and wireless routers. For the benefit of the research community, we also share some lessons learned during our experiences in the design, building and evaluation of DistressNet.

A wireless sensor network can get separated into multiple connected components due to the failure of some of its nodes, which is called a “cut.” In this paper, we consider the problem of detecting cuts by the remaining nodes of a wireless sensor network. We propose an algorithm that allows 1) every node to detect when the connectivity to a specially designated node has been lost, and 2) one or more nodes (that are connected to the special node after the cut) to detect the occurrence of the cut. The algorithm is distributed and asynchronous: every node needs to communicate with only those nodes that are within its communication range. The algorithm is based on the iterative computation of a fictitious “electrical potential” of the nodes. The convergence rate of the underlying iterative scheme is independent of the size and structure of the network. We demonstrate the effectiveness of the proposed algorithm through simulations and a real hardware implementation.

Localization of wireless sensor nodes has long been regarded as a problem that is difficult to solve, especially when considering characteristics of real-world environments. This paper formally describes, designs, implements, and evaluates a novel localization system called Spotlight. The system uses spatiotemporal properties of well-controlled events in the network, light in this case, to obtain locations of sensor nodes. Performance of the system is evaluated through deployments of Mica2 and XSM motes in an outdoor environment, where 20 cm localization error is achieved. A sensor network consisting of any number of nodes deployed in a 2,500 m2area can be localized in under 10 minutes. Submeter localization error in an outdoor environment is made possible without equipping the wireless sensor nodes with specialized ranging hardware.

In this paper we present RE2MR, an energy efficient and robust multicast routing protocol suitable for large scale real-world WSN deployments. RE2MR, a hybrid multicast protocol, builds on the strengths of existing topology-based, hierarchical and geographic multicast solutions, and addresses their limitations. RE2MR establishes a network topology in which multicast member nodes are connected to the root node via near-optimal multicast routing paths. RE2MR discovers deployment area irregularities (e.g., holes) that affect the optimality of multicast routing and considers them when recomputing the near-optimal solution. RE2MR incurs little computational overhead on forwarding nodes, a negligible communication overhead and ensures reliable multicast packet delivery. We implement RE2MR in Tiny OS and evaluate it extensively using TOSSIM. RE2MR reduces the energy consumption by up to 57% and the end-to-end delay by up to 8%, when compared with state of art solutions.

Wireless Sensor Networks (WSNs) often suffer from disrupted connectivity caused by its numerous aspects such as limited battery power of a node and unattended operation vulnerable to hostile tampering. The disruption of connectivity, often referred to as network cut, leads to ill-informed routing decisions, data loss, and waste of energy. A number of protocols have been proposed to efficiently detect network cuts, they focus solely on a cut that disconnects nodes from the base station. However, a cut detection scheme is truly useful when a cut is defined with respect to multiple destinations (i.e., target nodes), rather than a single base station. Thus, we extend the existing notion of cut detection, and propose an algorithm that enables sensor nodes to autonomously monitor the connectivity to multiple target nodes. We introduce a novel reactive cut detection solution, the Point-to-Point Cut Detection, where given any pair of source and destination, a source is able to locally determine whether the destination is reachable or not. Furthermore, we propose a lightweight proactive cut detection algorithm specifically designed for a small set of target destinations. We prove the effectiveness of the proposed algorithms through extensive simulations.

The problem of cooperative intrusion detection in resource constrained wireless networks (e.g., adhoc, sensor) is challenging, primarily because of the limited resources available to participating nodes. Although the problem has received some attention from the research community, little is known about the tradeoffs among different objectives, e.g. network performance, power consumption, delay in information being collected and security effectiveness. This paper proposes, to the best of our knowledge for the first time, to distribute cooperative intrusion detection functions that take into account, simultaneously, multiple objectives. We formulate the problem of identifying the type of intrusion detection each node runs as a multi-objective optimization problem and motivate/develop a genetic algorithm to solve it. Through extensive simulations we demonstrate that our solution is characterized by: a small variance in the normalized fitness values of individual/single objectives; and a smaller attack detection and reporting delay than state of art solutions. In a real implementation/evaluation of our cooperative intrusion detection system, we demonstrate that it achieves a higher detection rate (93%) than state of art solutions (60%-73%).

Neighbor discovery is an important part of many protocols for wireless adhoc networks, including localization and routing. When neighbor discovery fails, communications and protocols performance deteriorate. In networks affected by relay attacks, also known as wormholes, the failure may be more subtle. The wormhole may selectively deny or degrade communications. IIn this paper we present Mobile Secure Neighbor Discovery (MSND), which offers a measure of protection against wormholes by allowing participating mobile nodes to securely determine if they are neighbors. To the best of our knowledge, this work is the first to secure neighbor discovery in mobile adhoc networks. MSND leverages concepts of graph rigidity for wormhole detection.We prove security properties of our protocol, and demonstrate its effectiveness through extensive simulations and a real system evaluation employing Epic motes and iRobot robots.

Network flooding is a fundamental communication primitive for Wireless Sensor Networks (WSN). Flooding is used for disseminating code updates and parameter changes, affecting the operation of all nodes in the network. When flooding occurs each node, typically, broadcasts the flooding packet once. The costs for flooding, however, can become significant if neighborhood keys are used for communication (as proposed in recent research on secure localization and key distribution [1]), since, instead of a single broadcast, a node is required to perform several unicast transmissions. In this paper we address the problem of minimizing the number of unicast transmissions required for ensuring 100% network coverage for flooding in WSN secured with neighborhood keys. We show that the problem is NP-hard and propose an approximation algorithm for solving it. Through simulations, we demonstrate that our algorithm ensures 100% network coverage for flooding, while requiring, surprisingly, as low as 0.75 packet transmissions per node.

Geographic routing is well suited for large scale sensor networks deployments, because the per node state it maintains is independent of the network size. However, due to the “local minimum” caused by holes/obstacles, the path stretch of geographic routing can grow as O(c2), where c is the length of the optimal path. Recently, VIGOR, a geographic routing protocol based on the visibility graph, shows that a constant path stretch can be achieved. This, however, is possible with increased overhead. To address this issue, we propose GOAL (Geometric Routing using Abstracted Holes), a routing protocol that provably achieves a constant path stretch, with lower message, space and computational overhead. We develop a novel distributed convex hull construction (DCC) algorithm that compactly describes holes. This compact representation of a hole is leveraged by nodes to make locally optimal routing decisions. Our theoretical analysis proves the constant stretch property and average stretch of GOAL. Through extensive simulations and a hardware implementation, we demonstrate the effectiveness of GOAL and its feasibility for large-scale sensor networks. In our network settings, GOAL reduces the energy consumption by up to 32%, routing table size by an order of magnitude, when compared with VIGOR.

Saving energy while providing end-to-end delay guarantees and robust operation have long been regarded as of paramount importance in real-time adhoc and sensor networks. In this paper we explore how rate-adaptation can save energy in adhoc and sensor networks that have real-time requirements, and how robustness requirements, achieved by multipath routing, affect the achievable energy savings. We formulate the problem of finding the most energy efficient data rate for each link, propose an adaptive data rate selection algorithm, and demonstrate that our scheme can save up to 15 % energy, when compared with state of art, while still meeting the end-to-end delay guarantees.

In many applications of wireless sensor networks, sensor nodes are manually deployed in hostile environments where an attacker can disrupt the localization service and tamper with legitimate in-network communication. In this paper, we introduce Secure Walking GPS, a secure localization and key distribution solution for manual deployments of WSNs. Using the location information provided by the GPS and inertial guidance modules on a special master node, Secure Walking GPS achieves accurate node localization and location-based key distribution at the same time. Our analysis and simulation results indicate that the Secure Walking GPS scheme makes a deployed WSN resistant to the Dolev-Yao, the wormhole, and the GPS-denial attacks, has good localization and key distribution performance, and is practical for large-scale WSN deployments.

Data streaming is an emerging class of applications for sensor networks that has very high bandwidth and processing power requirements. In this paper, a new approach for speech data streaming is proposed, which is based on a distributed scheme. This scheme focuses on balancing the energy consumption among nodes in a sensor network by allowing low- resolution streams from multiple nodes to be fused at a central processing node in order to produce an enhanced resolution speech signal. Simulations and experimental results with real microphone signals are presented.

Energy efficiency is a fundamental issue for outdoor sensor network systems. This article presents the design and implementation of multidimensional power management strategies in VigilNet, a major recent effort to support long-term surveillance using power-constrained sensor devices. A novel tripwire service is integrated with an effective sentry and duty cycle scheduling in order to increase the system lifetime, collaboratively. The tripwire service partitions a network into distinct, nonoverlapping sections and allows each section to be scheduled independently. Sentry scheduling selects a subset of nodes, the sentries, which are turned on while the remaining nodes save energy. Duty cycle scheduling allows the active sentries themselves to be turned on and off, further lowering the average power draw. The multidimensional power management strategies proposed in this article were fully implemented within a real sensor network system using the XSM platform. We evaluate key system parameters using a network of 200 XSM nodes in an outdoor environment, and an analytical probabilistic model. We evaluate network lifetime using a simulation of a 10,000-node network that uses measured XSM power values. These evaluations demonstrate the effectiveness of our integrated approach and identify a set of lessons and guidelines, useful for the future development of energy-efficient sensor systems. One of the key results indicates that the combination of the three presented power management techniques is able to increase the lifetime of a realistic network from 4 days to 200 days.

In this paper, we advocate asymmetric function placement as one of guiding principles to architect sensor network systems. We demonstrate its generic applicability and effectiveness by applying this principle to three typical sensor network technologies, namely, localization (Spotlight), sensing (uSense) and communication (mNets). These technologies have very dissimilar features, representing a wide spectrum of system design requirements. We have invested significant effort to design, implement and evaluate our techniques on TinyOS/Mote testbeds. The results from several running systems indicate that asymmetric function placement is a powerful guiding principle to achieveefficiencyandhigh-performancesimultaneously in wireless sensor networks. At the end, we exam the system features that discourage the use of asymmetric function placement and approaches to address them.

Loss of connectivity in deployed wireless sensor networks can be quite disastrous for the network. A "cut" (which separates the network into two or more components incapable of communicating with each other) is usually hard to detect. An algorithm which enables each node in the network to detect whether a cut has occurred anywhere in the network is demonstrated.

Abstract — Energy efficiency is a fundamental issue for out-door sensor network systems. This paper presents the design and implementation of multi-dimensional power management strategies in VigilNet, a major recent effort to support long-term surveillance using power-constrained sensor devices. We integrate a novel tripwire service with an effective sentry and duty cycle scheduling in order to increase the system lifetime, collaboratively. Through extensive system implementation, we demonstrate the feasibility to achieve high surveillance perfor-mance and energy efficiency, simultaneously. We invest a fair amount of effort to evaluate our architecture with a network of 200 XSM motes in an outdoor environment, an extensive simulation with 10,000 nodes, as well as an analytical probabilistic model. These evaluations demonstrate the effectiveness of our integrated approach and identify many interesting lessons and guidelines, useful for the future development of energy-efficient sensor systems. I.

The problem of localization in wireless sensor networks where nodes do not use ranging hardware, remains a challenging problem, when considering the required location accuracy, energy expenditure and the duration of the localization phase. In this paper we propose a framework, called StarDust, for wireless sensor network localization based on passive optical components. In the StarDust framework, sensor nodes are equipped with optical retro-reflectors. An aerial device projects light towards the deployed sensor network, and records an image of the reflected light. An image processing algorithmis developed for obtaining the locations of sensor nodes. For matching a node ID to a location we propose a constraint-based label relaxation algorithm. We propose and develop localization techniques based on four types of constraints: node color, neighbor information, deployment time for a node and deployment location for a node. We evaluate the performance of a localization system based on our framework by localizing a network of 26 sensor nodes deployed in a 120-60ft2 area. The localization accuracy ranges from 2 ft to 5 ft while the localization time ranges from 10 milliseconds to 2 minutes.

This article describes one of the major efforts in the sensor network community to build an integrated sensor network system for surveillance missions. The focus of this effort is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice, and energy consumption is narrowly accounted for within a single protocol. In this article, we describe the design and implementation of a complete running system, called VigilNet, for energy-efficient surveillance. The VigilNet allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We evaluate VigilNet middleware components and integrated system extensively on a network of 70 MICA2 motes. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such an integrated sensor system.

Battlefield surveillance often involves a high element of risk for military operators. Hence, it is very important for the military to execute unmanned surveillance by using large-scale wireless sensor systems. This invited paper summarizes the architecture of the VigilNet system - a long-term real-time networked sensor system for military surveillance. Specifically, we review the design of several major subsystems within VigilNet including sensing and classification, localization, tracking, networking, power management, reconfiguration, graphic user interface, and the debugging subsystem. High-level programming abstractions are also presented. This is a balanced design to achieve realtime response, high confidence detection, accurate tracking and energy efficiency simultaneously.

No abstract available.

The problem of localization of wireless sensor nodes has long been regarded as very difficult to solve, when considering the realities of real world environments. In this paper, we formally describe, design, implement and evaluate a novel localization system, called Spotlight. Our system uses the spatio-temporal properties of well controlled events in the network (e.g., light), to obtain the locations of sensor nodes. We demonstrate that a high accuracy in localization can be achieved without the aid of expensive hardware on the sensor nodes, as required by other localization systems. We evaluate the performance of our system in deployments of Mica2 and XSM motes. Through performance evaluations of a real system deployed outdoors, we obtain a 20cm localization error. A sensor network, with any number of nodes, deployed in a 2500m2 area, can be localized in under 10 minutes, using a device that costs less than $1000. To the best of our knowledge, this is the first report of a sub-meter localization error, obtained in an outdoor environment, without equipping the wireless sensor nodes with specialized ranging hardware.

Distributed sensor networks are quickly gaining recognition as viable embedded computing platforms. Current techniques for programming sensor networks are cumbersome, inflexible, and low-level. We introduce EnviroTrack, an object-based distributed middleware system that raises the level of programming abstraction by providing a convenient and powerful interface to the application developer geared towards tracking the physical environment. EnviroTrack is novel in its seamless integration of objects that live in physical time and space into the computational environment of the application. Performance results demonstrate the ability of the middleware to track realistic targets.

We present the design, implementation and evaluation of a simple, practical and cost effective localization solution, called walking GPS, that can be used in real, manual deployments of wireless sensor networks. We evaluate our localization solution exclusively in real deployments of MICA2 and XSM motes. Our experiments show that 100% of the deployed motes localize (i.e,. have a location position) and that the average localization errors are within 1 to 2 meters, due mainly to the limitations of the existing commercial GPS devices.

The focus of surveillance missions is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice and energy consumption is narrowly accounted for within a single protocol. In this paper, we describe the design and implementation of a running system for energy-efficient surveillance. The system allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We can trade off energy-awareness and surveillance performance by adaptively adjusting the sensitivity of the system. We evaluate the performance on a network of 70 MICA2 motes equipped with dual-axis magnetometers. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such a complete running system.

Location information is of paramount importance for wireless sensor networks (WSN). The accuracy of the collected data can significantly be affected by an imprecise positioning of the event of interest. Despite the importance of location information, real system implementations that do not use specialized hardware for localization purposes have not been successful. In this paper, we propose a location estimation scheme that uses a probabilistic approach for estimating the location of a node in a sensor network. Our localization scheme makes use of additional knowledge of topology deployment. We assume a sensor network is deployed in a controlled manner, where the goal of the deployment is to form a grid topology. We evaluate our localization scheme through simulations, showing localization errors as low as 3% of radio range. We outperform similar localization schemes by obtaining 50% less error in localization. We also evaluate our localization solution and the DV-hop scheme in a real implementation, obtaining an average error in location of 79% of radio range, outperforming DV-hop by approximately 40%. We analyze the significant differences in performance between simulations and a real implementation and stress the importance of further evaluations of real implementations. The result is an effective and realistic protocol that works in an actual implementation, under certain assumptions, because it exploits deployment information.