Wireless Rechargeable Sensor Networks (WRSNs) promise to address the limited energy resource issue for sensor nodes through wireless power transfer technology. However, WRSNs are vulnerable to various security threats, such as compromised node attack and malicious mobile charger (MC) attack, which can disrupt the charging process and degrade charging efficiency. In this work, we investigate the eneRgy conversionEfficiency maximization problem unDer chargIng attackS(REDIS). We propose a blockchain-based framework that employs a lightweight multi-layer storage approach tailored for resource-constrained sensor nodes and features consensus algorithms that validate charging transactions. Furthermore, we introduce a validation node selection strategy that integrates consensus execution with charging scheduling, reducing energy consumption, and improving energy efficiency. Extensive simulations and experiments validate the effectiveness of our framework, improving energy efficiency by 30% and as much as 5 times in networks without attacks and those under full attacks, respectively.

Sparse CrowdSensing has emerged as a promising paradigm for data collection, which utilizes mobile devices to gather partial sensing data and infer the remaining ones. Most existing studies focus on data inference. However, they often overlook the impact of unforeseen circumstances. These perturbations can lead to many issues in the data inference process such as over-correction (adjustments are excessively applied), perturbation diffusion (errors affecting related data), and data bias (overall trends in inferred data deviating from the truth). To address the aforementioned challenges, we propose a novel approach called Gradual Matrix Completion (GMC) for handling perturbations in Sparse CrowdSensing. This method distinguishes itself by inferring adjacent unsensed data and using it as a basis for ongoing optimization, gradually completing the entire data matrix. Through the gradual inference process, GMC mitigates perturbation diffusion by continuously refining feature selection, thus preserving and reconstructing essential features. Moreover, GMC concentrates on both intra-area and inter-area spatio-temporal relationships, progressively enhancing its understanding of local and global dependencies. Evaluation of the GMC approach on four diverse real-world datasets highlights its robust capability to complete perturbed data and cope with noise in Sparse CrowdSensing.

Graph neural networks and graph transformers explicitly or implicitly rely on fundamental properties of the underlying graph, such as spectral properties and shortest-path distances. However, it is still not clear how these graph properties are vulnerable to adversarial attacks and what impacts this has on the downstream graph learning. Moreover, while graph sparsification has been used to improve computational cost of learning over graphs, its susceptibility to adversarial attacks has not been studied. In this paper, we study adversarial attacks on graph properties and graph sparsification and their impacts on downstream graph learning, paving the way for how to protect against these potential attacks. Our proposed methods are effective in attacking spectral properties, shortest distances, and graph sparsification as demonstrated in our experimental evaluation.

Despite the impressive capabilities of Large Language Models (LLMs), their tendency to generate hallucinations, i.e., factually incorrect or nonsensical content, remains a major challenge for reliable deployment, especially in high-stakes fields such as healthcare, law, and scientific research. Detecting and reducing these hallucinations is critical for building more trustworthy systems. In this work, we propose a hallucination detection framework using a custom-designed classifier architecture that leverages late internal representations from LLM. We explore three distinct approaches for classifier input: (i) features from the Multi-Layer Perceptron (MLP) blocks, (ii) features from the Attention blocks, and (iii) a combination of both. Experiments across eight different LLMs and different evaluator scores show that our classifier, termed LIR, consistently outperforms other state-of-the-art approaches in AUC, correlation with human annotations, and response generation time. Our results demonstrate the effectiveness of using late internal representations for hallucination detection and contribute to the development of standardized benchmarks to evaluate the reliability of generative models.

Presents corrections to the paper, (Corrections to “Giant Could Be Tiny: Efficient Inference of Giant Models on Resource-Constrained UAVs”).

Underwater Optical Wireless Sensor Networks (UOWSNs) play important roles in resource exploration and maritime rescue. However, they face significant challenges in real-time data transmission due to the limited propagation range of optical signals (typically 10-100 m), frequent link disconnections caused by node mobility, and the extended distances to onshore servers. Traditional cloud computing solutions, designed for stable terrestrial networks with stationary edge servers and continuous connectivity, experience high latency (3-15 s) in UOWSNs, rendering them unsuitable for real-time applications in underwater environments. To address this issue, we propose a cloud-edge-end architecture tailored for UOWSNs, which can not only combat unique underwater environmental interference on link connection and topological changes but also guarantee robust and real-time communication. We develop a dynamic link-stability-based task offloading path selection (DLS-TOPS) algorithm for maximizing network resource profits. Afterward, we propose an online primal-dual task offloading (OPD-TO) algorithm for minimizing task completion time. Simulation results indicate that the proposed method significantly improves the real-time performance and resource profits of the network, reducing the total task completion time by more than 50% compared to baseline algorithms. We implemented a UOWSN with a cloud-edge-end architecture using commercial off-the-shelf and verified the applicability and effectiveness of the proposed scheme in emergency detection through testbed experiments.

Mobilecrowdsensing (MCS) is a powerful technique that enables a variety of urban tasks, including temperature monitoring, location-based services, and urban path recommendations. However, these tasks often face the challenge of sparse and incomplete sensing data, undermining their effectiveness and reliability.Sparsedatacompletion (SDC) methods have been developed to infer missing or unobserved data by leveraging spatio-temporal correlations to tackle this issue. This forms the core concept of thesparsemobilecrowdsensing problem (SMCS), which aims to improve the performance of downstream tasks through inferred data. Despite the potential benefits, most existing SMCS methods fail to consider the trade-off between the cost of SDC and the benefits for downstream tasks. These methods often treat SDC and downstream tasks as independent modules, resulting in suboptimal outcomes. In this paper, we investigate the impact of SDC on the SMCS paradigm, both qualitatively and quantitatively. We establish the upper bound of performance achievable when applying SDC in SMCS under different levels of sensing data sparsity. Based on these studies and findings, we propose a practical and flexible framework calledSDC-EVA,SensingDataCompletionEVAluation framework. This framework allows for applying different SDC methods in SMCS, considering factors such as computing complexity, storage space, and associated costs. Our proposed framework allows researchers to assess the necessity and feasibility of integrating SDC into SMCS systems before designing and deploying them in real-world scenarios. This assessment can be tailored to specific data sparsity and contextual information. To validate the effectiveness of our proposed evaluation framework, we conduct experiments in various real-world scenarios involving different combinations of SDC and downstream tasks. The results demonstrate the superiority of our framework in improving the performance of SMCS. By presenting these findings, we aim to contribute to developing SMCS techniques and provide valuable insights for researchers and practitioners.

Giant models, characterized by their billions or even trillions of parameters, has demonstrated unprecedented capabilities in handling complex tasks on Artificial intelligence (AI)-driven UAVs, such as disaster relief, aerial navigation, and manipulation. However, there is an open challenge about the mismatching between the massive computation and memory requirements of giant models and the limited resources on UAVs. Existing works either pose privacy concerns with offloading methods or compromise model accuracy with various model compression techniques. In this paper, we fill the gap by exploiting the Mixture-of-Expert (MoE) model architecture that decouples giant models into multiple tiny experts, so that UAVs can dynamically load a few experts that best match their current input. We consider a general scenario of several edge servers feeding experts to multiple UVAs and formulate a core problem of expert selection and UAV-edge association. Due to the high complexity of this problem, we propose a solution, termed GESolver, based on graph learning, which automatically solves the problem by learning the complicated interaction between edge servers, UAVs, as well as their required experts. We evaluate our proposed method with three popular MoE-based models under various problem settings. The experiments demonstrate that our proposed method can significantly outperform other baselines.

In order to mitigate the performance degradation of intermittent vehicular network caused by traffic mobility and sporadic connectivity issues, Vehicular Content Centric Network (VCCN) has been proposed to apply many technologies in Content Centric Network (CCN) into vehicular ad hoc networks. The open in-network caching strategy of CCN enables sharing and coordination of the cached data among multiple nodes as an efficient data access without relying on remote fetching. Nonetheless, few studies have considered the effective use of overall cache capacity with these cooperative nodes, especially the issues of cache duplication. Furthermore, most content replacement polices have ignored the needs of cooperative contents when making cache decisions. In this paper, we design a novel Cooperative Caching scheme by using Mobility Prediction and Consistent Hash for VCCN (called CCMPCH). Specifically, based on the observation that vehicles with the same trajectory are more likely to maintain stable communication links, we adopt Prediction by Partial Matching (PPM) to forecast each vehicle’s path and cluster the vehicles with similar future path, moving direction, and moving speed into one group. In each cluster, the consistent hash algorithm is used to allocate contents among cooperative nodes, thereby reducing unnecessary cache duplication while maintaining strong content availability. A popularity-based cache replacement policy is also developed to prioritize cooperative contents. We evaluate CCMPCH via extensive simulations, which demonstrates its higher cache hit ratio, shorter content access delay, and lower hop count compared to other state-of-the-art schemes.

Localization services play an important role in Internet of Connected Vehicles (IoCV) and vehicle predictive localization information can greatly improve traffic efficiency and reduce accidents. However, a huge amount of computing and communication overhead is required to obtain such information by traditional methods. In this work, we propose a Behavior-based Clustering Method (BCM) to analyze the behavioral correlation between vehicles and classify them into different clusters. Based on BCM results coupled with a deep learning model, we further propose a Clustering-learning-based Long-term Predictive Localization (CLPL) algorithm to predict vehicles' future location distribution. In the proposed CLPL algorithm, all the traffic roads are divided into consecutive small segments in order to pinpoint vehicles' precise current locations and to obtain long-term predictions. Extensive simulations, notably involving real dataset, have been carried out to evaluate BCM and CLPL in terms of several performance criteria including matching rates. The analysis of the results validated how the designed methods can predict vehicle location much more accurately than existing algorithms.

Recent breakthrough in wireless power transfer technology has enabled wireless sensor networks to operate virtually forever with the help of mobile chargers (MCs), thus generating the concept of wireless rechargeable sensor networks (WRSNs). However, existing studies mainly focus on developing charging tours with fixed network topology, most of which are not suitable for networks with dynamic topology, usually leading to massive packet/data loss. In this work, we explore the problem of charging scheduling for WRSNs with multiple MCs when confronting with dynamic topology. To minimize the energy cost to prolong the network lifetime, we convert the charging scheduling problem into a vehicle routing problem, which is proved to be NP-hard. Then we model the problem as a cooperative game taken among sensors and propose a cooperative game theoretical charging scheduling (CGTCS) algorithm to construct the optimal coalition structure. Then, we design an adaptive optimal coalition structure updating algorithm (AOCSU) to update the optimal coalition structure, which works well with network dynamics. We discuss the reasonability and feasibility to guarantee the cooperation among sensors through carefully designing the characteristic function and allocating cost based on Shapley value. Finally, test-bed experiments and simulations are conducted, revealing that CGTCS outperforms other related works in terms of expenditure ratio, total traveling cost, and charging time.

Named Data Network (NDN), as a promising information-centric networking architecture, is expected to support next-generation of large-scale content distribution with open in-network cachings. However, such open in-network caches are vulnerable against Cache Pollution Attacks (CPAs) with the goal of filling cache storage with non-popular contents. The detection and defense against such attacks are especially difficult because of CPA's similarities with normal fluctuations of content requests. In this work, we use a clustering technique to detect and defend against CPAs. By clustering the content interests, our scheme is able to distinguish whether they have followed the Zipf-like distribution or not for accurate detections. Once any attack is detected, an attack table will be updated to record the abnormal requests. While such requests are still forwarded, the corresponding content chunks are not cached. Extensive simulations in ndnSIM demonstrate that our scheme can resist CPA effectively with higher cache hit, higher detecting ratio, lower hop count, and lower algorithm complexity compared to other state-of-the-art schemes.

Named Data Network (NDN) is an alternative to host-centric networking exemplified by today's Internet. One key feature of NDN is in-network caching that reduces access delay and query overhead by caching popular contents at the source as well as at a few other nodes. Unfortunately, in-network caching suffers various privacy risks by different attacks, one of which is termed timing attack. This is an attack to infer whether a consumer has recently requested certain contents based on the time difference between the delivery time of those contents that are currently cached and those that are not cached. In order to prevent the privacy leakage and resist such kind of attacks, we propose a detection scheme by adopting Long Short-term Memory (LSTM) model. Based on the four input features of LSTM, cache hit ratio, average request interval, request frequency, and types of requested contents, we timely capture more important eigenvalues by dividing a constant time window size into a few small slices in order to detect timing attacks accurately. We have performed extensive simulations to compare our scheme with several other state-of-the-art schemes in classification accuracy, detection ratio, false alarm ratio, and F-measure. It has been shown that our scheme possesses a better performance in all cases studied.

The discovery of Wireless Power Transfer (WPT) technologies makes charging more convenient and reliable. Among all the existing WPT technologies, directional WPT is more efficient and has been successfully applied to supply energy for wireless rechargeable sensor networks (WRSNs). However, the state-of-the-art methods ignore the anisotropic energy receiving property of rechargeable sensors, resulting in energy wastage. In order to address this issue, in this paper, we point out that the received energy of a sensor is not only relative to the distance, but also relative to the angle between the sensor and the charger's orientation in directional WPT. Towards this end, we derive a pragmatic energy transfer model verified by experiments. In particular, we focus on a Minimal chArging Delay (MAD) problem to reduce charging delays. To obtain the optimal solution, we formulate the problem as a linear programming problem. Moreover, we introduce a method of charging power discretization, which significantly reduces the search space and bounds the performance gap to the optimal one with a 1/1-ϵ2approximation ratio. Besides, a merging method is introduced for a more practical application scenario. Finally, we demonstrate that our methods outperform the Set Cover baseline method by an average of 34.2% through simulations and experiments.

Wireless Rechargeable Sensor Networks (WRSNs) with perpetual network lifetime have been used in many Internet of Things (IoT) applications, like smart city and precision agriculture. Rechargeable sensors together with Unmanned Aerial Vehicles (UAVs) are collaboratively employed for fulfilling periodic coverage tasks. However, traditional coverage solutions are normally based on static deployment of sensors and not suitable for such coverage requirements. In this paper, we propose a new concept of coverage problem named Period-Area Coverage (PAC) which requires data of the overall area must be collected periodically. We focus on maximizing the energy efficiency of UAVs and propose two heuristic scheduling schemes to balance energy cost. Moreover, we adopt adjustable sensing range to further promote efficiency and develop a charging re-allocation mechanism for UAVs. Test-bed experiments and extensive simulations demonstrate that the proposed schemes can enhance energy efficiency by 18.2% compared to prior arts.

Traditional approaches to secret key establishment based on common randomness have been based on certain restrictive assumptions, such as considering the available common randomness to consist of independent and identically distributed (i.i.d) repetitions of correlated random variables. Unfortunately, the i.i.d assumption does not generally reflect the conditions of real-life scenarios. For this reason, the current paper investigates the key-establishment potential of a more pragmatic model, in which all parties have access to imperfect information about a common source modeled as a Markov chain. Each party's information thus comes in the form of a hidden Markov model and, since the different parties share the same underlying Markov chain, we call the overall model a sibling hidden Markov model (SHMM). This paper studies upper and lower bounds on the secret key capacity for various types of SHMM. The difficulty of the problem emerges from its prohibitive computational cost. To address this obstacle, we represent the joint probability of the observations as the L1norm of a Markov random matrix, and use its convergence to a Lyapunov exponent.

Motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems, supporting delay-constrained traffic become critical for the communication system. In delay-constrained traffic, each packet has a hard deadline and if it cannot be delivered before its deadline, it becomes useless and will be removed from the system. In this work, we consider a slotted ALOHA system where multiple stations need to deliver delay-constrained traffic to a common receiver by accessing a shared channel. We prove that, under the frame-synchronized traffic pattern, the maximum system timely throughput converges to 1/e = 36.8% as the number of stations goes to infinity, which is the same as the asymptotic maximum system throughput for delay-unconstrained slotted ALOHA system with saturate traffic. While this is not completely surprising, we further investigate the speed of such a maximum system throughput approaching 1/e under borderline traffic.

With the rise of wireless power transfer technology, charging scheduling issue is prevalent in wireless rechargeable sensor networks (WRSNs). Most prior arts focused on two-dimensional (2-D) networks with homogeneous mobile chargers. However, three-dimensional (3-D) networks with collaborations among heterogeneous mobile chargers are more practical. In this paper, we consider 3-D networks in which wireless charging vehicles (WCVs) are employed with unmanned aerial vehicles (UAVs). To prolong network lifetime, we focus on device sleep time and energy usage and propose a 3-D Dynamic Collaborative Scheduling scheme (3DCS). Theoretical values of energy threshold and partition number are determined to assign charging tasks to chargers. Then, scheduling algorithms that include target selection, infeasibility test, and target update, are developed. In addition, a collaborative algorithm is developed to re-assign charging tasks from busy chargers toward their neighboring chargers to further improve charging efficiency. Test-bed experiments and extensive simulations reveal that, compared with several distinguished scheduling schemes, our scheme has a superior performance in charging throughput, energy efficiency, and other characteristics.

Benefited from recent breakthrough in wireless power transfer technology, the lifetime of wireless sensor networks (WSNs) can be prolonged significantly, generating the concept of wireless rechargeable sensor networks (WRSNs). While most recent works have been focusing on WRSNs with a single wireless charging vehicle (WCV), we investigate the issue of multiple WCVs' on-line collaborative charging schedules in this work. In our design, termed mTS, the network area is divided into subdomains for designated WCVs. Each WCV schedules its charging scheduling path by responding to the interdependency of temporal and spatial correlations from different charging requests. Higher priorities are given to sensor requests with a mixture of closer charging deadlines and closer distances. We further analyze the system performance with an M/M/n/mTS queueing model. Our further study through simulations revealed that our scheme excels in successful charging rate, sensor survival rate, and other related performance metrics. Our field experiments further confirmed these results and showed some further interesting findings on different charging hardware and methods.

Recently, wireless power transfer is emerging as an enabling technology of wireless rechargeable sensor networks. Conventional methods that charge each sensor until its battery is full take unproportionally long time to finish, due to the limitation of charging efficiency and power transfer technologies. In this paper, we propose a mixed partial and full charge (MPF) scheme, including three specialized modules, i.e., evaluation module, adjustment module, and selection module. MPF allows nodes to be replenished "partially" by a mobile charging vehicle (MCV). When executing adjustment module, a concept of power and path adjustment window is proposed for determining a proper power allocation scheme as well as charging path. Then a scheduling strategy termed return mechanism is designed to further utilize the energy of the MCV and improve effective energy utilization. Finally, we build a high-accuracy charging test-bed and evaluate the applicability as well as performance of the proposed scheme. For large-scale networks, we also perform simulations to demonstrate the effectiveness of MPF in promoting survival rate and reducing traveling distance of the MCV.

Secret key establishment from common randomness has been traditionally investigated under cartain limiting assumptions, of which the most ubiquitous appears to be that the information available to all parties comes in the form of independent and identically distributed (i.i.d.) samples of some correlated random variables. Unfortunately, models employing the i.i.d assumption are often not accurate representations of real scenarios. A more capable model would represent the available information as correlated hidden Markov models (HMMs), based on the same underlying Markov chain. Such a model accurately reflects the scenario where all parties have access to imperfect observations of the same source random process, exhibiting a certain time dependency. In this paper, we derive a computationally-efficient asymptotic converse bound for the secret key capacity of the correlated-HMM scenario. The main obstacle, not only for our model, but also for other non-i.i.d cases, is the computational complexity. We address this by converting the initial bound to a product of Markov random matrices, and using recent results regarding its convergence to a Lyapunov exponent. The methods developed in the paper are easily extensible to derive a secret-key capacity lower bound.

Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian's locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.

In this paper, we study both topological and algebraic properties of unrooted Gaussian trees in order to characterize their security performance. Such performance is measured by the corresponding potential in extracting common randomness from a given tree, which is further determined by max-min and min-max conditional mutual information (CMI) values, subject to the order of selecting variables from the tree by legitimate nodes Alice and Bob, and an eavesdropper Eve, respectively. A new operation is proposed to transform a Gaussian tree into another, and also to order different Gaussian trees. Through such operation we construct several equivalent classes of Gaussian trees. Each class includes multiple Gaussian trees that can be partially ordered based on the associated max-min or min-max CMI metric, and thus, we can find the most secure and the least secure trees in each partially ordered set (poset). The union of all posets generates all possible non-isomorphic trees of the given number of variables. Then, we assign a particular polynomial to each Gaussian tree, and show that such polynomial can determine the relative security performance of the Gaussian tree with respect to other trees within the same class. In the end, based on a generalized integer partition method, we propose a novel approach to efficiently enumerate the most secure structures of all posets.

Establishing secret common randomness between two or multiple devices in a network resides at the root of communication security. In its most frequent form of key establishment, the problem is traditionally decomposed into a randomness generation stage (randomness purity is subject to employing often costly true random number generators) and an information-exchange agreement stage, which relies either on public-key infrastructure or on symmetric encryption (key wrapping). In this paper, we propose a secret-common-randomness establishment algorithm for ad hoc networks, which works by harvesting randomness directly from the network routing metadata, thus achieving both pure randomness generation and (implicitly) secret-key agreement. Our algorithm relies on the route discovery phase of an ad hoc network employing the dynamic source routing protocol, is lightweight, and requires relatively little communication overhead. The algorithm is evaluated for various network parameters in an OPNET ad hoc network simulator. Our results show that, in just 10 min, thousands of secret random bits can be generated network-wide, between different pairs in a network of 50 users.

The lack of pre-existing infrastructure or dynamic topology makes it impossible to establish end-to-end connections in opportunistic networks (OppNets). Instead, a store-and-forward strategy can be employed. However, such loosely knit routing paths depend heavily on the cooperation among participating nodes. Selfish or malicious behaviors of nodes impact greatly on the network performance. In this paper, we design and validate a dynamic trust management model for secure routing optimization. We propose the concept of incorporating social trust into the routing decision process and design a trust routing based on social similarity (TRSS) scheme. TRSS is based on the observation that nodes move around and contact each other according to their common interests or social similarities. A node sharing more social features in social history record with the destination is more likely to travel close to the latter in the near future and should be chosen as the next-hop forwarder. Furthermore, social trust can be established based on an observed node's trustworthiness and its encounter history. Based on direct and recommended trust, those untrustworthy nodes will be detected and purged from the trusted list. Since only trusted nodes' packets will be forwarded, the selfish nodes have the incentives to behave well again. Simulation evaluation demonstrates that TRSS is very effective in detecting selfish or even malicious nodes and achieving better performance.

In this paper, our objective is to find out how topological and algebraic properties of unrooted Gaussian tree models determine their security robustness, which is measured by our proposed max-min information (MaMI) metric. Such metric quantifies the amount of common randomness extractable through public discussion between two legitimate nodes under an eavesdropper attack. We show some general topological properties that the desired max-min solutions shall satisfy. Under such properties, we develop conditions under which comparable trees are put together to form partially ordered sets (posets). Each poset contains the most favorable structure as the poset leader, and the least favorable structure. Then, we compute the Tutte-like polynomial for each tree in a poset in order to assign a polynomial to any tree in a poset. Moreover, we propose a novel method, based on restricted integer partitions, to effectively enumerate all poset leaders. The results not only help us understand the security strength of different Gaussian trees, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us both an algebraic and a topological perspective in grasping some fundamental properties of such models.

Due to the huge popularity of online social networks, many researchers focus on adding links, e.g., link prediction to help friend recommendation. So far, no research has been performed on link cuts. However, the spread of malware and misinformation can cause havoc and hence it is interesting to see how to cut links such that malware and misinformation will not run rampant. In fact, many online social networks can be modeled as undirected graphs. In this paper, we investigate different strategies to cut links among different users in undirected graphs so that the speed of virus and misinformation spread can be slowed down the most or even cut off. Two measures are chosen to evaluate the performance of these strategies: Average Inverse of Shortest Path Length (AIPL) and Rumor Saturation Rate (RSR). AIPL measures the communication efficiency of the whole graph while RSR checks the percentage of users receiving information within a certain time interval.

Extensive works have been undertaken to develop efficient statistical inference algorithms based on graphical models. However, there still lacks sufficient understanding about how topological properties affect certain information related metrics for certain graphs. In this paper, we are particularly interested in finding out how topological properties of rooted polytrees for Gaussian random variables determine its security robustness, which is measured by our proposed max-min information (MaMI) metric. MaMI is defined as the maximin value of the conditional mutual information between any two random variables (nodes) in a given DAG, conditioned on the value of a third random variable, which is at full disposal of an eavesdropper, under a constraint of a given fixed joint entropy. We show some general topological properties which the desired max-min solutions satisfy. Under such properties, we prove the superior max-min feature of the linear topology for a simple but non-trivial case. The results not only help us understand the security strength of different rooted polytree type DAGs, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us another algebraic and analysis perspective in grasping some fundamental properties of such DAGs.

Due to the disconnected and store-and-forward architecture in multimedia social networks (MSNs), routing becomes a great challenge with the frequent path disruptions. Moreover, some nodes in MSNs tend to be selfish or malicious, e.g. they sometimes will not forward packets for other nodes or will launch passive and active attacks in order to save their limited resources such as bandwidth, battery or storage. In order to address this issue, we propose a fuzzy-based trust management technique for context-based routing in MSNs. We incorporate social trust metrics and quality of service metrics into our trust model. By adopting fuzzy sets, every node can evaluate the credibility of other nodes based on the direct and indirect relationship. By ranking all its neighbors according to the trust values, each node can purge untrustworthy nodes. Since only trusted nodes’ packets will be forwarded, the selfish or malicious nodes have the incentive to behave well again in order to be able to send packets. Additionally, we perform extensive security and performance evaluation with the opportunistic network environment simulator. The simulation results show that our trust model can dynamically update the trust value in real time, effectively measure the trust relationship and correctly identify malicious or selfish nodes. Furthermore, the proposed trust routing is a lightweight protocol balancing the message overhead and delivery ratio.

Summary Wireless sensor networks (WSNs) are expected to be widely deployed to collect data in military and civilian applications. Because of the open nature of WSNs, it is easy for an adversary to eavesdrop sensor communication and to trace packets, causing privacy concern for the sensor devices. The privacy issue, especially location privacy, can be critical for monitoring applications in WSNs. A unique case of location privacy is that of the sources, which are vulnerable of being captured and target attacks. In this paper, we propose a scheme to protect the source–location privacy based on a novel use of multiring topology. To achieve a uniformly distributed traffic pattern throughout the network, the source node selects two random rings each from its external rings and internal rings and a set of two random angles with a sum of 180 degrees for each packet. The packet is sent at one of the angles in each ring. Fake packets are also injected to provide path diversity and to increase attack time, which is defined as the time that the adversary takes to locate the source successfully. These techniques protect the source node from packet tracing attacks as well as traffic analysis attacks. Our analysis and simulations, performed in the NS2 simulator and MATLAB, demonstrate that our proposed scheme can provide better spatial traffic evenness and longer attack time, along with a modest increase of hop count and energy consumption.Copyright © 2013 John Wiley & Sons, Ltd.

Multi-hop infrastructure-based communication is expected to play a vital role in supporting high data-rate multimedia access to mobile devices. The advantages are significant in highly mobile scenarios such as intra-vehicular networks. However, mobile nodes in these networks suffer from long authentication delays, which adversely affect the goodput. In this work, we propose two techniques to shorten the initial authentication delay without compromising the authentication process and overall security. One of the techniques, called fast authentication, admits data traffic temporarily through the network to the gateway and the immediate parent node of the joining node presents network-side authentication. The other technique, called prefetch-assisted authentication, allows the authenticated wireless nodes to prefetch and store the authentication vectors of the potential mobile clients. We investigate several unique features of our proposed schemes and find their performance to be suitable for infrastructure-based multi-hop wireless communications.

In this paper we study a transmission power-tune/control problem in the context of 802.11 Wireless Local Area Networks (WLANs) with multiple (and possibly densely deployed) access points (APs). Previous studies on power control tend to focus on one aspect of the control, either its effect on transmission capacity (PHY layer) assuming simultaneous transmissions, or its effect on contention order (MAC layer) by maximizing spatial reuse. We observe that power control has a dual effect: it affects both spatial reuse and capacity of active transmission; moreover, maximizing the two separately is not always aligned in maximizing system throughput and can even point in opposite directions. In this paper we introduce an optimization formulation that takes into account this dual effect, by measuring the impact of transmit power on system performance from both PHY and MAC layers. We show that such an optimization problem is intractable and develop an analytical framework to construct simple yet efficient solutions. Through numerical results, we observe clear benefits of this dual-effect model compared to solutions by trying to maximize spatial reuse and transmission capacity separately. This problem does not invoke cross-layer design, as the only degree of freedom in design resides with transmission power. It however highlights the complexity in tuning certain design parameters, as the change may manifest itself differently at different layers which may be at odds.

We focus on the problem of scalable shortest path analytics for large social graphs in this paper. While shortest path distance problem has been investigated extensively, massive graphs on social networks such as Facebook and Linked In call for reinvestigation of the problem due to the requirements of scalability and suitability for distributed computing. We propose a core-net based approach to address the problem. In our new core-net algorithm, popular nodes are selected based on their node degrees. Since some of these popular nodes may not be connected, we further include bridge nodes, which connect two or more popular nodes and improve their connectivity, but are not popular nodes themselves. Breadth First Search (BFS) technique is then used to compute shortest paths between any pair of nodes on the core net. When the shortest path between two arbitrary vertices, u and v, is queried, we approximate it with triangulation. We present a graph calculus theory in which the estimated distance goes to the real shortest distance when the degree threshold goes to zero. Analysis and simulation results confirm the superiority of our design, which can easily scale to MapReduce.

We evaluate the performance of an opportunistic multi-channel medium access control protocol and compare it to that of the corresponding single-channel MAC (S-MAC) and a non-opportunistic multi-channel MAC (M-MAC). We do this in three different settings: (1) an ideal scenario where no control channel is used and no sensing delay is incurred, (2) a more realistic scheme where users compete for access on a control channel using random access, and (3) a scheme similar to (2) but with a time-division multiplexing (TDM) based access scheme on the control channel. Our analysis and numerical results show that in terms of delay performance, the random access and competition on the control channel, which typically occupy a fraction of the total bandwidth, almost always wipe out the channel diversity gain, a main motivation behind an opportunistic multi-channel MAC. On the other hand opportunistic access increases bandwidth utilization which reduces the system's total busy time. As a result it helps reduce power consumption in general. When TDM is employed on the control channel, the data sub-channel sensing delay becomes the main bottleneck to attaining better performance. In this case the performance of opportunistic multi-channel MAC gets closer to that of the single-channel MAC when the channel sensing overhead is substantially reduced.

It is well known that communication systems are susceptible to strong impulsive noises. To combat this, convolutional coding has long served as a cost-efficient tool against moderately frequent memoryless impulses with given statistics. Nevertheless, impulsive noise statistics are difficult to model accurately and are typically not time-invariant, making the system design challenging. In this paper, because of the lack of knowledge regarding the probability density function of impulsive noises, an efficient decoding scheme was devised for single-carrier narrowband communication systems; a design parameter was incorporated into recently introduced joint erasure marking and Viterbi decoding algorithm, dubbed the metric erasure Viterbi algorithm (MEVA). The proposed scheme involves incorporating a well-designed clipping operation into a Viterbi algorithm, in which the clipping threshold must be appropriately set. In contrast to previous publications that have resorted to extensive simulations, in the proposed scheme, the bit error probability performance associated with the clipping threshold was characterized by deriving its Chernoff bound. The results indicated that when the clipping threshold was judiciously selected, the MEVA can be on par with its optimal maximum-likelihood decoding counterpart under fairly general circumstances.

Random key pre-distribution (RKPD) has been investigated for large wireless sensor networks, in order to achieve efficient security and robustness against limited node compromise. While it is possible that an adversary obtains a subset of the symmetric keys in use, it has been unclear how to use those to compromise specific secure links. We investigate how the adversary could do this practically. We term this the Stealthy Pre-Attack (SPA), because the adversarial nodes leverage benign behavior to guide their attack. The contribution of this paper is the identification of this adversarial behavior, the evaluation of its benefits for the attacker, which can then much more effectively compromise security, and the proposal of counter-measures to mitigate it.

In this paper we analyze the delay performance of an opportunistic multi-channel medium access control scheme and compare it to that of the corresponding single channel MAC scheme. In the opportunistic multi-channel MAC scheme, we assume that the pair of sender/receiver is able to evaluate the channel quality after a certain amount of channel sensing delay and to choose the best one for data communication. We consider three settings: (1) an ideal scenario where no control channel is needed and no sensing delay is incurred, (2) a more realistic scheme where users compete for access on a control channel using random access, and (3) a scheme similar to (2) but with a Time Division Multiplex (TDM) based access scheme on the control channel. Our analysis show that in terms of delay performance, the random access overhead on the control channel almost always wipe out the channel diversity gain, which is the main motivation behind an opportunistic multi-channel MAC. Using a TDM based access scheme on the control channel can help remove this bottleneck, but only when channel sensing can be done sufficiently fast.

When asymmetric cryptography techniques are used in wireless networks, the public keys of the nodes need to be widely available and signed by a Certificate Authority (CA). However, the existence of a single CA in large wireless networks such as mobile ad hoc networks and wireless sensor networks can lead the hotspot problem and become a security weakness. In this work, we propose a distributed technique to cache the public keys on regular nodes. Due to the limited memory size that each node is allowed to dedicate for key caching, only some keys can be cached. In our proposed technique, each node caches the public keys of a mix of local and remote nodes. Access to the public keys of other nodes is possible based on a chain of trust. Multiple copies of public keys from different chains of trusted nodes provide fault tolerance. We explain our technique in detail and investigate its salient features in this work. An interesting observation is the need to balance caching public keys of local nodes and remote nodes.

In this work, we focus on the problem of fault tolerant data collection in heterogeneous Intelligent Monitoring Networks(IMNs). IMNs are expected to have a wide range of applications in many fields such as forest monitoring, structural monitoring, and industrial plant monitoring. We present our fault tolerant data collection scheme in the hierarchical structure of IMNs. We use an interesting technique borrowed from the popular BitTorrent software to maintain a highly efficient and robust data collection in IMNs with heterogeneous and faulty devices. In our proposed scheme, monitoring sensors are instructed to randomly select some overheard transmissions and process them in data fusion. Our preliminary study confirmed the benefits of the fault tolerant data collection strategy.

Fairness index among competing hosts in communication networks is an important system measurement. Several fairness index measurements have been proposed in the technical literature. However, most of these measurements, such as the max/min fairness index and Jain's index, reflect only a long-term average fairness of the system. Instantaneous fairness property has not been captured. In this paper, we propose a new fairness index to reflect such short-term fairness and long-term fairness at the same time. Comparisons of our proposed fairness index, termed Fairness Index based on Variational Distance (FIVD), and related fairness indices are presented to show the benefit of our measurement.

The success of Wireless Sensor Networks (WSNs) depends largely on efficient information delivery from target areas toward data sinks. The problem of data forwarding is complicated by the severe energy constraints of sensors in WSNs. In this work, we propose and analyze a data forwarding scheme, termed Multihop/Direct Forwarding (MDF), for WSNs where sensor nodes forward data traffic toward a common data sink. In the MDF scheme, a node splits outgoing traffic into at most two branches: one is sent to a node that is h units away; the other is sent directly to the data sink. The value of h is chosen to minimize the overall energy consumption of the network. The direct transmission is employed to balance the energy consumption of nodes at different locations and to avoid the so-called hot spot problem in data forwarding. In order to calculate its traffic splitting ratio, a node only needs to know the distance toward the common data sink and that of the farthest node. Our analytical and simulation results show that the MDF scheme performs close to, in terms of energy efficiency and network lifetime, the optimum data forwarding rules, which are more complex and computation intensive.

We analyze the delay performance of RTS/CTS-based (Request-To-Send/Clear-To-Send) multi-channel MAC (Medium Access Control) schemes for wireless networks. These schemes usually employ multiple data subchannels for data transmission and one control subchannel to send the RTS/CTS dialogue for channel reservation. Through theoretical analysis and simulations, we show that, in fully-connected networks, such multi-channel MAC schemes suffer longer delays than the corresponding single channel MAC scheme, that puts the RTS/CTS dialogue on the same channel as data packet transmissions. This conclusion holds even when data packets have different priorities and higher priority traffic is sent ahead of lower priority traffic.

Failure-Rate Minimization is becoming one of the major design issues in wireless sensor network (WSN) architecture due to multiple available Functional-units (FUs). There is a tradeoff between reliability and performance, such as timing constraint. This paper studies how to minimize the total failure rate while satisfying performance requirement for WSN applications. Two novel algorithms are proposed to solve the FRMFS (Failure Rate Minimization with FU Scheduling) problem. We use these FU scheduling algorithms to minimize system failure rate without sacrificing performance. Our results show that the average improvement on failure-rate reduction is significant with the use of our algorithms.

In many medium access control (MAC) schemes for wireless networks, an Acknowledgment (ACK) packet is transmitted from the data receiver to the data sender to announce the successful reception of the data packet. Such a protocol requirement may become a system weakness when malicious nodes attack these wireless networks. In this paper, we demonstrate the effects of such a Jamming ACK (JACK) attack to networks employing the popular carrier sense multiple access with collision avoidance (CSMA/CA) scheme in IEEE 802.11 DCF. Our study shows that a JACK attacker can easily disrupt the traffic flow between two wireless nodes when it sends out JACK packets at the right time. The benefits of such a JACK attack include low energy consumption by the attacker, attack stealthiness, and great damage to the victim nodes. To mitigate the effects of JACK attacks, we propose in this paper an extended network allocation vector (ENAV) scheme. Our analysis and simulations show that the ENAV scheme recovers a significant portion of the lost throughput and reduces the energy drainage of the attacked nodes to 40%.

The trend toward high processing power at a reasonable cost continues with the emergence of multi-core architectures with large number of cores. In such computing systems, a major technological challenge is to design the internal, on-chip communication network.This not only depends on high performance in latency, bandwidth, and fairness in contention under heavy loads, but also depends on an efficient authentication mechanism and protection of integrity and privacy of applications from hardware and software attacks. In this paper, we present a technique to establish secret keys for the cores employed by the same application to communicate securely. Our approach is based on key pre-distribution, in which each of the cores chooses a number of keys from a large key pool. When a group of cores are employed by an application, they discover their shared keys and establish a group key for secured communication. The key discovery and the process to connect partitioned groups use space-efficient Bloom filter to ensure the security and efficiency of the key establishment process. Our performance evaluation demonstrates the efficiency of the proposed framework.

In random key predistribution techniques for wireless sensor networks, a relatively small number of keys are randomly chosen from a large key pool and are loaded on the sensors prior to deployment. After deployment, each sensor tries finding a common key shared by itself and each of its neighbors to establish a link key to protect the wireless communication between themselves. One intrinsic disadvantage of such techniques is that some neighboring sensors do not share any common key. In order to establish a link key among these neighbors, a multihop secure path may be used to deliver the secret. Unfortunately, the possibility of sensors being compromised on the path may render such an establishment process insecure. In this work, we propose and analyze the just-enough redundancy transmission (JERT) scheme that uses the powerful maximum-distance separable (MDS) codes to address the problem. In the JERT scheme, the secret link key is encoded in (n, k) MDS code and transmitted through multiple multihop paths. To reduce the total information that needs to be transmitted, the redundant symbols of the MDS codes are transmitted only if the destination fails to decode the secret. The JERT scheme is demonstrated to be efficient and resilient against node capture. One salient feature of the JERT scheme is its flexibility of trading transmission for lower information disclosure.

Broadcast is a fundamental operation in wireless networks and naive flooding is not practical because it cannot deal with interference. Scheduling is a good way to avoid interference, but previous studies on broadcast scheduling algorithms all assume highly theoretical models such as the unit disk graph model. In this work, we re-investigate this problem using the 2-disk and the signal-to-interference-plus-noise-ratio (SINR) model to realize it. We first design a constant approximation algorithm for the 2-disk model and then extend it to the SINR model. This result is the first result on broadcast scheduling algorithms in SINR model, to the best of our knowledge.

In Wireless Sensor Networks (WSNs), symmetric key schemes may be used to provide security. Recently, a class of random key pre-distribution techniques have been proposed and investigated. Such techniques only guarantee to establish keys for some pairs of physically connected sensors. In this work, we address the issue of delivering secret link keys to each of the source's neighbors in wireless sensor networks. We propose a scheme called Babel that finds a common bridge node to deliver one key to each of the to-be-connected neighbors. The novelty of our scheme is to deliver multiple keys through a common bridge node and regular paths instead of multi-hop secure paths. Since the delivered keys are only disclosed to one node, the common bridge node, key compromise probability of the Babel scheme is significantly lower compared to other delivery techniques.

Known covert channel based on splitting algorithms in Medium Access Control (MAC) protocols requires the receiver's knowledge of the sender's identity. In this paper we present a new covert channel that does not have this restriction. In such a channel, multiple senders may operate independently without knowing each other, and the receiver can learn the transmitted information without knowing the identity of any covert sender a priori. These properties make the channel robust to malfunctioning senders, and more importantly help protect the secrecy of senders' identity which is essential for covert communications. We also analyze the capacity of our proposed covert channel.

The transmission range that achieves the most economical use of energy in wirelessad hocnetworks is studied for uniformly distributed network nodes. By assuming the existence of forwarding neighbors and the knowledge of their locations, the average per-hop packet progress for a transmission range that is universal for all nodes is derived. This progress is then used to identify the optimal per-hop transmission range that gives the maximal energy efficiency. Equipped with this analytical result, the relation between the most energy-economical transmission range and the node density, as well as the path loss exponent, is numerically investigated. It is observed that when the path loss exponent is high (such as four), the optimal transmission ranges are almost identical over the range of node densities that we studied. However, when the path loss exponent is only two, the optimal transmission range decreases noticeably as the node density increases. Simulation results also confirm the optimality of the per-hop transmission range, which we found analytically.

The transmission range that achieves the most economical use of energy in wireless ad hoc networks is studied for uniformly distributed network nodes. By assuming the existence of forwarding neighbors and the knowledge of their locations, the average per-hop packet progress for a transmission range that is universal for all nodes is derived. This progress is then used to identify the optimal per-hop transmission range that gives the maximal energy efficiency. Equipped with this analytical result, the relation between the most energy-economical transmission range and the node density, as well as the path-loss exponent, is numerically investigated. It is observed that when the path-loss exponent is high (such as four), the optimal transmission ranges are almost identical over the range of node densities that we studied. However, when the path-loss exponent is only two, the optimal transmission range decreases noticeably as the node density increases. Simulation results also confirm the optimality of the per-hop transmission range that we found analytically.

We study routing misbehavior in MANETs (mobile ad hoc networks) in this paper. In general, routing protocols for MANETs are designed based on the assumption that all participating nodes are fully cooperative. However, due to the open structure and scarcely available battery-based energy, node misbehaviors may exist. One such routing misbehavior is that some selfish nodes will participate in the route discovery and maintenance processes but refuse to forward data packets. In this paper, we propose the 2ACK scheme that serves as an add-on technique for routing schemes to detect routing misbehavior and to mitigate their adverse effect. The main idea of the 2ACK scheme is to send two-hop acknowledgment packets in the opposite direction of the routing path. In order to reduce additional routing overhead, only a fraction of the received data packets are acknowledged in the 2ACK scheme. Analytical and simulation results are presented to evaluate the performance of the proposed scheme

In mobile ad hoc networks (MANETs), the hidden and the exposed terminal problems affect the throughput of the medium access control (MAC) protocols. Several MAC schemes have been proposed to solve these problems by allowing the exposed terminals to send their packets concurrent with the on-going transmission. Due to their locations, the exposed terminals are likely to have data packets for neighbors that are temporarily unavailable to receive. This leads to the so- called head-of-line (HOL) blocking problem. In this work, we propose the exposed terminal scheduling (ETS) technique to solve the problem. In the ETS scheme, all nodes collect the local topology and transmission information to make sure that they will not send to those temporarily unavailable nodes. The ETS shuffle (ETSS) scheme is proposed to mitigate the problem when such local topology and transmission information are unavailable. Simulation results are presented to support the claimed benefits of the proposed techniques.

To achieve security in wireless sensor networks, it is important to be able to encrypt messages sent among sensor nodes. Keys for encryption purposes must be agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is nontrivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key-based schemes, are not suitable for wireless sensor networks. Predistribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key predistribution scheme and its improvements have been proposed. A common assumption made by these random key predistribution schemes is that no deployment knowledge is available. Noticing that, in many practical scenarios, certain deployment knowledge may be available a priori, we propose a novel random key predistribution scheme that exploits deployment knowledge and avoids unnecessary key assignments. We show that the performance (including connectivity, memory usage, and network resilience against node capture) of sensor networks can be substantially improved with the use of our proposed scheme. The scheme and its detailed performance evaluation are presented in this paper.

In this work, we analyze and evaluate the maximum achievable throughput of split-channel MAC schemes that are based on the RTS/CTS (ready-to-send/clear-to-send) dialogue and that rely on pure ALOHA or on p-persistent carrier sensing multiple access (CSMA) contention resolution techniques. Our results show that, when radio propagation delays are negligible and when the pure ALOHA mechanism is used, then for a network with relatively large number of nodes, the maximum achievable throughput of the split-channel MAC schemes is lower than that of the corresponding single-channel MAC schemes. When the split-channel MAC schemes employ the p-persistent CSMA mechanism, then they out-perform the corresponding single-channel schemes when the maximum end-to-end propagation delays are at least 25% of the transmission time of the control packets on the single shared channel.

In order to improve the throughput performance of medium access control (MAC) schemes in wireless communication networks, some researchers proposed to divide a single shared channel into several sub-channels: one as control sub-channel and the others as data sub-channels. In this paper, we analyze and evaluate the maximum achievable throughput of a class of generic multi-channel MAC schemes that are based on the RTS/CTS (ready-to-send/clear-to-send) dialogue and on ALOHA contention resolution. We study these multi-channel MAC schemes under two split-channel scenarios: the fixed-total-bandwidth scenario and the fixed-channel-bandwidth scenario. In the fixed-total-bandwidth scenario, we show that the throughput of the multi-channel MAC schemes is inferior to that of the corresponding single-channel MAC scheme, which sends the RTS/CTS packets and DATA packets on a single shared channel. For the fixed-channel-bandwidth scenario, where CDMA or similar techniques can be applied, we derive the optimal number of the data sub-channels that maximizes the throughput. The analytical framework that we derive in this paper can also be used to evaluate other contention resolution technique, when the average contention period is known.

In this paper, we propose guided broadcast algorithms (GB) - a family of distributed broadcast algorithms for wireless sensor networks (WSNs). GB is based on the recently proposed self-organizing redundancy cellular architecture (SoRCA) for WSNs. It utilizes spatial diversity to cover the sensor nodes in the WSN for a tunable number of times to ensure that at least n portion, a parameter specified by QoS-requirements, of nodes will receive the broadcast message (BM), instead of striving for a 100% delivery ratio which can be costly and unnecessary in some WSN applications. Specifically, the coverage assurance algorithm (CAA) in the GB family ensures that, in the ideal scenario, all nodes in the WSN receive any BM with probability that is at least n; the propagation assurance algorithm (PAA) is invoked to circumvent obstacles and to ensure that BMs propagate through the entire WSN. Additionally, the load balancing and equal exposure scheme (LBEE) is proposed to balance broadcast energy cost. Our performance evaluations show that the proposed schemes possess several nice characteristics that fit well for many applications of WSNs

Abstract — Contention-based Geographic Forwarding (CGF) is a state-free communication paradigm for information delivery in multihop ad hoc and sensor networks. A priori selection of the forwarding area impacts its overall network performance and the design of the CGF protocol as well. In this work, we study the fundamental problem of defining the forwarding area apriorifor CGF and determine its impact on the performance. We model CGF without void (i.e., absence of a next-hop node in the forwarding area) handling as a 3-step forwarding strategy. Based on this model and given a random distribution of network nodes, we develop a general mathematical analysis technique to evaluate the performance of CGF with different forwarding areas, in terms of the performance metric average single-hop packet progress. Further, we introduce two state-free void handling schemes, i.e., active exploration and passive participation, for CGF and study their performance in depth. Our theoretical analysis and numerically evaluated results, validated by extensive simulations, provide a guideline regarding the selection of specific forwarding areas for the design of a practical CGF protocol. It also serves as a general performance evaluation framework for the existing CGF protocols. I.

Mobile ad hoc networks (MANETs) operate on the basic underlying assumption that all participating nodes fully collaborate in self-organizing functions. However, performing network functions consumes energy and other resources. Therefore, some network nodes may decide against cooperating with others. Providing these selfish nodes, also termed misbehaving nodes, with an incentive to cooperate has been an active research area recently. In this paper, we propose two network-layer acknowledgment-based schemes, termed the TWOACK and the S-TWOACK schemes, which can be simply added-on to any source routing protocol. The TWOACK scheme detects such misbehaving nodes, and then seeks to alleviate the problem by notifying the routing protocol to avoid them in future routes. Details of the two schemes and our evaluation results based on simulations are presented in this paper. We have found that, in a network where up to 40% of the nodes may be misbehaving, the TWOACK scheme results in 20% improvement in packet delivery ratio, with a reasonable additional routing overhead.

A novel, state-free, and competition-based data delivery protocol, called state-free implicit forwarding (SIF), is proposed for multihop wireless sensor networks. The SIF protocol assumes moderate node density and distance-to-sink awareness. The state-free feature of SIF makes it robust to high network dynamics. SIF also combines the tasks of routing and MAC, via cross-layer design, to simplify the complexity of the protocol stack in sensors and to save precious network resources. Simulation results are presented to show that SIF performs better than some previously proposed protocols for data delivery in terms of communication overhead, packet delivery ratio, and average packet delay.

To achieve security in wireless sensor networks, it is important to be able to encrypt and authenticate messages sent between sensor nodes. Before doing so, keys for performing encryption and authentication must be agreed upon by the communicating parties. Due to resource constraints, however, achieving key agreement in wireless sensor networks is nontrivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and other public-key based schemes, are not suitable for wireless sensor networks due to the limited computational abilities of the sensor nodes. Predistribution of secret keys for all pairs of nodes is not viable due to the large amount of memory this requires when the network size is large.In this paper, we provide a framework in which to study the security of key predistribution schemes, propose a new key predistribution scheme which substantially improves the resilience of the network compared to previous schemes, and give an in-depth analysis of our scheme in terms of network resilience and associated overhead. Our scheme exhibits a nice threshold property: when the number of compromised nodes is less than the threshold, the probability that communications between any additional nodes are compromised is close to zero. This desirable property lowers the initial payoff of smaller-scale network breaches to an adversary, and makes it necessary for the adversary to attack a large fraction of the network before it can achieve any significant gain.

We investigate the effects of the carrier sensing range of the IEEE 802.11 multiple access control (MAC) scheme in this paper. Contrary to the simple and inaccurate cut-off circular collision model that is commonly used, we employ a more accurate collision model to realistically simulate MAC schemes in ad hoc networks. We argue that the carrier sensing range is a tunable parameter that can significantly affect the MAC performance in multihop ad hoc networks. An optimal carrier sensing range should balance the trade-off between the amount of spatial frequency reuse and the possibility of packet collisions. A reward formulation for the optimization of the carrier sensing range is presented. Extensive simulation results are provided to substantiate our study.

To achieve security in wireless sensor networks, it is important to he able to encrypt messages sent among sensor nodes. Keys for encryption purposes must he agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is nontrivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key pre-distribution scheme and its improvements have been proposed. A common assumption made by these random key pre-distribution schemes is that no deployment knowledge is available. Noticing that in many practical scenarios, certain deployment knowledge may be available a priori, we propose a novel random key pre-distribution scheme that exploits deployment knowledge and avoids unnecessary key assignments. We show that the performance (including connectivity, memory usage, and network resilience against node capture) of sensor networks can he substantially improved with the use of our proposed scheme. The scheme and its detailed performance evaluation are presented in this paper.

The transmission range that achieves the most economical use of energy in wireless ad hoc networks is studied under homogeneous node distribution. By assuming the knowledge of node location, we first proposed a transmission strategy to ensure the progress of data packets toward their final destinations. Then the average packet progress for a transmission range universal for all nodes is derived, which is accordingly used to determine the optimal transmission range that gives the maximum efficiency of energy consumption. Different from some previous work, our analysis does not make the assumption of large nodal density in the wireless ad hoc networks studied. Numerical and simulation results are presented to examine our analysis for wireless ad hoc networks.

To achieve security in wireless sensor networks, it is important to be able to encrypt and authenticate messages sent among sensor nodes. Keys for encryption and authentication purposes must be agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is non-trivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. To solve the key pre-distribution problem, two elegant key pre-distribution approaches have been proposed recently [11, 7].In this paper, we propose a new key pre-distribution scheme, which substantially improves the resilience of the network compared to the existing schemes. Our scheme exhibits a nice threshold property: when the number of compromised nodes is less than the threshold, the probability that any nodes other than these compromised nodes is affected is close to zero. This desirable property lowers the initial payoff of smaller scale network breaches to an adversary, and makes it necessary for the adversary to attack a significant proportion of the network. We also present an in depth analysis of our scheme in terms of network resilience and associated overhead.

In wireless sensor networks, sensor nodes are spread randomly over the coverage area to collect information of interest. Data fusion is used to process these collected information before they are sent to the base station, the observer of the sensor network. We study the security of the data fusion process in this work. In particular, we propose a witness-based solution to assure the validation of the data sent from data fusion nodes to the base station. We also present the theoretical analysis for the overhead associated with the mechanism, which indicates that even in an extremely harsh environment the overhead is low for the proposed mechanism.

To improve channel throughput and the fairness of random access channels, we propose a new backoff algorithm, namely, the sensing backoff algorithm (SBA). A novel feature of the SBA scheme is the sensing mechanism, in which every node modifies its backoff interval according to the results of the sensed channel activities. In particular, every active node sensing a successful transmission decreases its backoff interval by an additive factor of the transmission time of a packet. In order to find the optimum parameters for the SBA scheme, we have studied the optimum backoff intervals as a function of different numbers of active nodes (N) in a single transmission area with pure ALOHA-type channels. We find that the optimum backoff interval should be 4N times the packet transmission time when the random access channel operates under a pure ALOHA scheme. Based on this result, we have calculated numerically the optimum values of the parameters for SBA, which are independent of N. The SBA scheme operates close to the optimum backoff interval. Furthermore, its operation does not depend on a knowledge of N. The optimum backoff interval and the SBA scheme have been studied also by simulation. It is shown that the SBA scheme out-performs other backoff schemes, such as binary exponential backoff (BEB) and multiplicative increase linear decrease (MILD). As a point of reference, the SBA scheme offers a channel capacity of 0.19 when N is 10, while the MILD scheme can only offer 0.125. The performance gain is about 50%.

In ad hoc networks, each node utilizes its limited resources to carry out the collective operation of the network. It is not always in the best interests of the network's nodes to demand the continuous participation of all nodes in the network operations. We propose an energy dependent participation (EDP) scheme, where a node periodically re-evaluates its participation in the network based on the residual energy in its battery. More importantly, a node gives special consideration to supporting the communication needs of its active network applications and preventing further network partitioning. EDP's localized partition checking algorithm is particularly well suited for the zone routing protocol, where the link-state information is proactively maintained within each node's local zone and routes to faraway nodes are reactively obtained via global queries. Through simulations, we evaluate the impact of our proposed scheme on battery life and network connectivity. Our results suggest that the EDP scheme can increase the usable lifetime of a battery-constraint ad hoc network by over 50%.

In ad hoc networks, the hidden- and the exposed-terminal problems can severely reduce the network capacity on the MAC layer. To address these problems, the ready-to-send and clear-to-send (RTS/CTS) dialogue has been proposed in the literature. However, MAC schemes using only the RTS/CTS dialogue cannot completely solve the hidden and the exposed terminal problems, as pure "packet sensing" MAC schemes are not safe even in fully connected networks. We propose a new MAC protocol, termed the dual busy tone multiple access (DBTMA) scheme. The operation of the DBTMA protocol is based on the RTS packet and two narrow-bandwidth, out-of-band busy tones. With the use of the RTS packet and the receive busy tone, which is set up by the receiver, our scheme completely solves the hidden- and the exposed-terminal problems. The busy tone, which is set up by the transmitter, provides protection for the RTS packets, increasing the probability of successful RTS reception and, consequently, increasing the throughput. This paper outlines the operation rules of the DBTMA scheme and analyzes its performance. Simulation results are also provided to support the analytical results. It is concluded that the DBTMA protocol is superior to other schemes that rely on the RTS/CTS dialogue on a single channel or to those that rely on a single busy tone. As a point of reference, the DBTMA scheme out-performs FAMA-NCS by 20-40% in our simulations using the network topologies borrowed from the FAMA-NCS paper. In an ad hoc network with a large coverage area, DBTMA achieves performance gain of 140% over FAMA-NCS and performance gain of 20% over RI-BTMA.

The dual busy tone multiple access (DBTMA) scheme was designed for distributed multi-hop networks. The protocol uses the RTS/CTS dialogue to reserve the shared channel. In multi-hop networks, some nodes in the range of the transmitter and/or the range of the receiver might not be able to hear a successful RTS/CTS message exchange. This may lead to access collisions and data destruction. Hence we use two narrow bandwidth busy tones, to notify neighbor nodes of the on-going use of the channel. In this paper, we analyze the capacity of the DBTMA protocol. The effects of various parameters on the network utilization in multi-hop networks are also discussed. We compare, through analytical results and simulation means, the performance of the DBTMA protocol with schemes that solely use the RTS/CTS dialogue to prevent collisions and we show that DBTMA provides superior performance to such schemes.