The wireless sensor network is a resource-constrained self-organizing system that consists of a large number of tiny sensor nodes. Due to the low-cost and low-power nature of sensor nodes, sensor nodes are failure-prone when sensing and processing data. Most presented fault-tolerant research for wireless sensor networks focused on crash faults or power faults and less on Byzantine faults. Hence, in this paper, we propose a power-saving data aggregation algorithm for Byzantine faults to provide power savings and high success rates even in the environment with high fault rates. The algorithm utilizes the concept of Byzantine masking quorum systems to mask the erroneous values and to finally determine the correct value. Our simulation results demonstrate that when the fault rate of sensor nodes is up to 50%, our algorithm still has 48% success rate to obtain the correct value. Under the same condition, other fault-tolerant algorithms are almost failed.

Secure sensor localization is a prerequisite for many sensor networks to retrieve trustworthy data. However, most of existing node positioning systems were studied in trust environment and are therefore vulnerable to malicious attacks. In this work, we develop a robust node positioning mechanism(ROPM) to protect localization techniques from position attacks. Instead of introducing countermeasures for every possible internal or external attack, our approach aims at making node positioning system attack-tolerant by removing malicious beacons. We defeat internal attackers and external attackers by applying different strategies, which not only achieves robustness to attacks but also dramatically reduces the computation overhead. Finally, we provide detailed theoretical analysis and simulations to evaluate the proposed technique.

We analyze the performance of an opportunistic transmission strategy for Wireless Sensor Networks (WSNs). We consider a transmission strategy called Binary Decision-Based Transmission (BDT), which is a common form of opportunistic transmission. The BDT scheme initiates transmission only when the channel quality exceeds the optimum threshold to avoid unsuccessful transmissions that waste energy. We formulate the Markov Decision Process (MDP) to identify an optimum threshold for transmission decisions in the BDT scheme.

This letter proposes a simple modification of LEACH protocol to exploit its multi-hop scenario for user cooperation. Instead of a single cluster-head we propose M cluster-heads in each cluster to obtain the diversity of order M. All cluster-heads gather data from all sensor nodes within the cluster using the same technique as LEACH. Cluster-heads transmit gathered data cooperatively towards the destination or higher order cluster-head. We propose a code combining based cooperative protocol. We also develop the upper bounds on frame error rate (FER) for our proposal. Simulation and analysis show that our proposal can significantly prolong the system lifetime.

Recently, the result of TinyECCK (Tiny Elliptic Curve Cryptosystem with Koblitz curve) shows that both field multiplication and reduction over GF(2m) are related to a heavy amount of duplicated memory accesses and that reducing the number of these duplications noticeably improves the performance of elliptic curve operations such as scalar multiplications, signing and verification. However, in case that the underlying word size is extended from 8-bit to 16-bit or 32-bit, the efficiency of the techniques proposed in TinyECCK is decreased because the number of memory accesses to load or store an element in GF(2m) is significantly reduced. Therefore, in this paper, we propose a technique which makes left-to-right (ltr) comb method which is widely used as an efficient multiplication algorithm over GF(2m) suitable for extended word sizes and present TinyECCK16 (Tiny Elliptic Curve Cryptosystem with Koblitz curve on 16-bit word) which is implemented with the proposed multiplication algorithm on 16-bit Tmote Sky mote. The proposed algorithm is faster than typical ltr comb method by 15.06% and the 16-bit version of the algorithm proposed in TinyECCK by 5.12% over GF(2163).

In this letter, we propose a novel approach using wireless sensor networks (WSNs) to enhance the safety and efficiency of four-way stop-sign-controlled (FWSC) intersections. The proposed algorithm provides right of way (RoW) and crash avoidance information by means of an intelligent WSN system. The system is composed of magnetic sensors, embedded in the center of a lane, with relay nodes and a base station placed on the side of the road. The experimental results show that the vehicle detection accuracy is over 99% and the sensor node battery life expectancy is over 3 years for traffic of 5,800 vehicles per day. For the traffic application we consider, a strong effect is observed as the projected conflict rate was reduced by 72% compared to an FWSC intersection operated with only driver perception.

In the proposed scheme, every sensor establishes communications keys with its neighbors after deployment. They are selectively employed for intra-cluster communications, and the employed keys are determined by local topology of clusters. Thus, our scheme periodically changes the local topology of clusters so as to renew the intra-cluster communication keys. Besides, new Cluster Heads (CHs) easily share a key with the Base Station (BS) by informing the BS of their member information without sending key materials. Simulation results prove that our approach has strong resiliency against the increase of compromised sensors. It also achieves a performance gain in terms of energy.

Wireless sensor networks (WSNs) have attracted a significant amount of interest from many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have a wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with such resource limitations as battery capacity, memory, CPU, and communication capacity are deployed without control in a region and used to monitor and gather sensor information of environments. Therefore, a scalable and efficient network control and/or data gathering scheme for saving energy consumption of each sensor node is needed to prolong WSN lifetime. In this paper, assuming that sensor nodes synchronize to intermittently communicate with each other only when they are active for realizing the long-term employment of WSNs, we propose a new synchronization scheme for gathering sensor information using chaotic pulse-coupled neural networks (CPCNN). We evaluate the proposed scheme using computer simulations and discuss its development potential. In simulation experiments, the proposed scheme is compared with a previous synchronization scheme based on a pulse-coupled oscillator model to verify its effectiveness.

This letter presents the variation of localization error to network parameters, the number of range estimation results from anchor nodes (ANs) and average distance between ANs in centralized Wireless Sensor Network (WSN). In sensor network, ANs estimate the relative range to Target Node (TN) using Time-Of-Arrival (TOA) information of Ultra WideBand (UWB) radio and a fusion center determines the final localization of TN based on estimation results reported. From simulation results, the variation of localization error, which is defined as the difference between localization result of TN and its actual location, is represented as the function of number of estimation results to average distance between ANs. The distribution of localization error is matched to the Rician distribution whose K-factor value is given by the proposed formula as well. Finally, the normalized error function for the efficient localization network design is characterized.

For successful data collection in wireless sensor networks, it is important to ensure that the required delivery ratio is maintained while keeping a fair rate for every sensor. Furthermore, emerging high-rate applications might require complete reliability and the transfer of large volume of data, where persistent congestion might occur. These requirements demand a complete but efficient solution for data transport in sensor networks which reliably transports data from many sources to one or more sinks, avoids congestion and maintains fairness. In this paper, we propose congestion-aware and rate-controlled reliable transport (CRRT), an efficient and low-overhead data transport mechanism for sensor networks. CRRT uses efficient MAC retransmission to increase one-hop reliability and end-to-end retransmission for loss recovery. It also controls the total rate of the sources centrally, avoids the congestion in the bottleneck based on congestion notifications from intermediate nodes and centrally assigns the rate to the sources based on rate assignment policy of the applications. Performance of CRRT is evaluated in NS-2 and simulation results demonstrate the effectiveness of CRRT.

In wireless sensor networks (WSNs), localization using the received signal strength (RSS) method is famous for easy adaptation and low cost where measuring the distance between sensor nodes. However, in real localization systems, the RSS is strongly affected by many surrounding factors and tends to be unstable, so that it degrades accuracy in distance measurement. In this paper, we propose the angle-referred calibration based RSS method where angle relation between sensor nodes is used to perform the calibration for better performance in distance measurement. As a result, the proposed scheme shows that it can provide high precision.

CSMA/CA is a well known medium access mechanism extensively used in wireless networks. By detecting the carrier sensing (CS) signal, nodes determine whether the status of the wireless medium is busy or idle. However, recent works have shown that besides detecting the channel status, these signals can be used to derive the transmitted packet size at the nodes in the CS range. In this paper, we present the feasibility of this technique using CC2420 radio. In addition, we show how we can apply larger CS range and packet size detection to solve well-known problems such as reducing latency in the wireless sensor network (WSN). To our knowledge, the proposed solution is the first trial that applies such techniques to design the delay-sensitive scheduling for WSN. Based on our ns-2 simulation, we show that our proposal reduces latency significantly compared to the existing listen/sleep scheduling based protocols.

Wide area ubiquitous wireless networks, which consist of access points (APs) connected to the fixed network and a great many wireless terminals (WTs), can offer a wide range of applications everywhere. In order to enhance network performance, we need to collect different kinds of data from as many WTs as possible; each AP must be capable of accommodating more than 103 WTs. This requirement can be achieved by employing DSA, a typical centralized media access control scheme, since it has high resource utilization efficiency. In this paper, we propose a novel DSA scheme that employs three new techniques to enhance throughput performance; (1) considering that most terminals tend to send data periodically, it employs both polling-based schemes, i.e. request-polling and data-polling, and a random access scheme. (2) In order to enhance bandwidth utilization effectiveness by polling, the polling timing is decided according to the data generation timing. (3) The AP decides the polled data size according to the latest distribution of data size and polls the WT for the data directly. If the data-polling size can not be determined with confidence, the AP uses request-polling instead of data-polling. Simulations verify that the proposed scheme offers better transmission performance than the existing schemes.

The ability to estimate a target location is essential in many applications of wireless sensor networks. Received signal strength indicator (RSSI)-based maximum likelihood (ML) method in a wireless sensor network usually requires a pre-determined statistical model on the variation of RSSI in a sensing area and uses it as an ML function when estimating the location of a target in the sensing area. However, when estimating the location of a target, due to several reasons, we often measure the RSSIs which do not follow the statistical model, in other words, which are outlier on the statistical model. As the result, the effect of the outlier RSSI data worsens the estimation accuracy. If the wireless sensor network has a lot of sensor nodes, we can improve the estimation accuracy intentionally rejecting such outlier RSSIs. In this paper, we propose a simple outlier RSSI data rejection algorithm for an ML location estimation. The proposed algorithm iteratively eliminates the anchor nodes which measure outlier RSSIs. As compared with the location estimation methods with previously proposed outlier RSSI data rejection algorithms, our proposed method performs better with much less computational complexity.

The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of monitoring continuous objects such as fires and hazardous bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with performance analysis and simulation results that total average report message size as well as the number of nodes which transmit the report messages to the sink can be greatly reduced, especially when the density of nodes over the network field is high.

An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.

Many applications in wireless sensor networks (WSNs) require the energy efficiency and scalability. Although IEEE 802.15.4/Zigbee which is being considered as general technology for WSNs enables the low duty-cycling with time synchronization of all the nodes in network, it still suffer from its low scalability due to the beacon frame collision. Recently, various algorithms to resolve this problem are proposed. However, their manners to implement are somewhat ambiguous and the degradation of energy/communication efficiency is serious by the additional overhead. This paper describes an Energy-efficient BOP-based Beacon transmission Scheduling (EBBS) algorithm. EBBS is the centralized approach, in which a resource-sufficient node called as Topology Management Center (TMC) allocates the time slots to transmit a beacon frame to the nodes and manages the active/sleep schedules of them. We also propose EBBS with Adaptive BOPL (EBBS-AB), to adjust the duration to transmit beacon frames in every beacon interval, adaptively. Simulation results show that by using the proposed algorithm, the energy efficiency and the throughput of whole network can be significantly improved. EBBS-AB is also more effective for the network performance when the nodes are uniformly deployed on the sensor field rather than the case of random topologies.

Wireless sensor network (WSN) is a promising approach for a variety of applications. Routing protocol for WSNs is very challenging because it should be simple, scalable, energy-efficient, and robust to deal with a very large number of nodes, and also self-configurable to node failures and changes of the network topology dynamically. Recently, many researchers have focused on developing hierarchical protocols for WSNs. However, most protocols in the literatures cannot scale well to large sensor networks and difficult to apply in the real applications. In this paper, we propose a novel adaptive routing protocol for WSNs called ARPEES. The main design features of the proposed method are: energy efficiency, dynamic event clustering, and multi-hop relay considering the trade-off relationship between the residual energy available of relay nodes and distance from the relay node to the base station. With a distributed and light overhead traffic approach, we spread energy consumption required for aggregating data and relaying them to different sensor nodes to prolong the lifetime of the whole network. In this method, we consider energy and distance as the parameters in the proposed function to select relay nodes and finally select the optimal path among cluster heads, relay nodes and the base station. The simulation results show that our routing protocol achieves better performance than other previous routing protocols.

Sensor node localization is an important issue in wireless sensor networks (WSNs) due to the dynamic nature of sensor deployment. Generally, in wireless sensor network localization, the absolute positions of certain anchor nodes are required based on the use of global positioning systems, then all the other nodes are approximately localized using various algorithms based on a coordinate system of the anchors. This paper proposes a neighbor position-based localization algorithm (NPLA) that can greatly enhance the positioning accuracy when compared with current overlapping connectivity localization algorithms that attempt to use the observation of connectivity to a set of anchors to determine a node's position. The proposed method localizes the sensor nodes using both the anchors' positions and neighbor node information. However, unlike existing overlapping-based methods, the proposed NPLA does not assume the same radio transmission range. A simulation study is used to demonstrate the positioning accuracy of the proposed method with different anchor and sensor node densities.

ZigBee recently has been used a lot in wireless sensor networks because of its low power consumption and affordable chips. However, ZigBee's existing hierarchical routing algorithm has a disadvantage in that a node may communicate with a nearby node over several hops. In this letter we propose a Quasi-Hierarchical Routing (QHR) algorithm that can improve the ZigBee hierarchical routing method's inefficiency by using brief information on neighbors within radio range. The network simulation evaluates this QHR's performance by comparing it to other ZigBee routing schemes.