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In-network collaborative computation is essential for implementation of a large number of sensor applications. We approach the problem of computation in sensor networks from a parallel and distributed system's perspective. We define COSMOS, the Cluster-based, heterOgeneouSMOdel for Sensor networks. The model abstracts the key features of the class of cluster-based sensor applications. It assumes a hierarchical network architecture comprising of a large number of low cost sensors with limited computation capability, and fewer number of powerful clusterheads, uniformly distributed in a two dimensional terrain. The sensors are organized into single hop clusters, each managed by a distinct clusterhead. The clusterheads are organized in a mesh-like topology. All sensors in a cluster are time synchronized, whereas the clusterheads communicate asynchronously. The sensors are assumed to have multiple power states and a wakeup mechanism to facilitate power management. To illustrate algorithm design using our model, we discuss implementation of algorithms for sorting and summing in sensor networks.

In this paper, we study the problem of maintaining sensing coverage by keeping a small number of active sensor nodes and using a small amount of energy consumption in wireless sensor networks. This paper extends a result from ²² where only uniform sensing range among all sensors is used. We adopt an approach that allows non-uniform sensing ranges for different sensors. As opposed to the uniform sensing range node scheduling model in ²², two new energy-efficient models with different sensing ranges are proposed. Our objective is to minimize the overlapped sensing area of sensor nodes, thus to reduce the overall energy consumption by sensing and communication to prolong the whole network's life time, and at the same time to achieve the high ratio of coverage. Extensive simulation is conducted to verify the effectiveness of our node scheduling models.

We consider the convergecast problem in wireless sensor networks where each sensor has a reading that must reach a designated sink. Since a sensor reading can usually be encoded in a few bytes, more than one reading can readily fit into a standard transmission packet. We assume that each packet hop consumes one unit of energy. Our objective is to minimize the total energy consumed to send all readings to the sink. We show that this problem is NP-hard even when all readings are of fixed size. We then study a class SPEP of distributed algorithms that is completely defined by two properties. Firstly, the packets hop along some shortest path to the sink. Secondly, the nodes use an elementary packing algorithm to pack readings into packets.

Our main technical contribution is a lower bound. We show that no algorithm for UCCP that either follows the shortest path or packs in an elementary manner is a (2 − ϵ)-approximation, for any fixed ϵ > 0. To complement this, we show that SPEP algorithms are -approximation for UCCP and 3-approximation for CCP, where k ≥ 2 is the number of readings that can fit within a packet. We conclude with some special cases and experimental observations.

Recently, a secure two-factor user authentication scheme for wireless sensor networks (WSN) was proposed by Das. The proposed scheme can resist many logged in users with the same login identity, stolen-verifier, guessing, impersonation and replay threats. In this paper we study the two-factor user authentication scheme for WSN and find that the scheme is insecure against the masquerade attacks. With our masquerade attacks, an attacker can masquerade as any legal user to login the gateway node without knowing the user's password or can masquerade as a gateway node to communicate with the legal user at anytime.

Given an arrangement A of n sensors and two points s and t in the plane, the barrier resilience of A with respect to s and t is the minimum number of sensors whose removal permits a path from s to t such that the path does not intersect the coverage region of any sensor in A. When the surveillance domain is the entire plane and sensor coverage regions are unit line segments, even with restricted orientations, the problem of determining the barrier resilience is known to be NP-hard. On the other hand, if sensor coverage regions are arbitrary lines, the problem has a trivial linear time solution. In this paper, we study the case where each sensor coverage region is an arbitrary ray, and give an O(n²m) time algorithm for computing the barrier resilience when there are m ⩾ 1 sensor intersections.

In this paper, we address the problem of estimating Gaussian mixtures in a sensor network. The scenario we consider is the following: a common signal is acquired by sensors, whose measurements are affected by standard Gaussian noise and by different offsets. The measurements can thus be statistically modeled as mixtures of Gaussians with equal variance and different expected values. The aim of the network is to achieve a common estimation of the signal, and to cluster the sensors according to their own offsets.

For this purpose, we develop an iterative, distributed, consensus-like algorithm based on Maximum Likelihood estimation, which is well-suited to work in-network when the communication to a central processing unit is not allowed. Estimation is performed by the sensors themselves, which typically consist of devices with limited computational capabilities.

Our main contribution is the analytical proof of the convergence of the algorithm. Our protocol is compared with existing methods via numerical simulations and the trade-offs between robustness, speed of convergence and implementation simplicity are discussed in detail.

A Wireless Sensor Network (WSN) is a distributed system populated by a base station and a number of wireless sensor nodes. The main task of the sensor nodes in a WSN is to monitor the environment, collect data, and eventually transfer the sensed data to the base station. For reliable monitoring, the sensor nodes must be in close proximity to the physical events. The sensor nodes are tiny devices that operate on a frugal energy budget and may fail dua to, among other causes, power depletion, catastrophic events, and external damages. When the location and the state (faulty or fault-free) of each sensors in the network is known, the faulty sensors can be repaired/recharged or new sensors could be added to the affected areas so as to maintain accurate monitoring. The main contribution of this work is to provide energy-efficient means to identify the location of the faulty sensor nodes in the WSN. Consider a WSN populated by n wireless sensor nodes which are arranged in a two-dimensional grid of size . Let q and k denote the number of fault-free and faulty nodes in the WSN, respectively. The task of identifying the faulty nodes and reporting the location of these faulty sensors to the base station can be completed in O(α + r²) time slots and none of the sensors need to awake for more than O(log log α) time slots, where α = min(q,k) and r is the transmission range of the sensor nodes.

In this paper we present Clustering Protocol for Sensor networks (CPS). Clustering techniques are used by different protocols and applications to increase scalability and reduce delays in sensor networks. Examples include routing protocols, and applications requiring efficient data aggregation. Our approach is based on the Covering Problem that aims at covering an area with minimum number of circular disks. CPS is a lightweight protocol that does not require any neighborhood information and imposes low communication overhead. We present simulation results to show the efficiency of CPS in both ideal cases and randomly distributed networks. Moreover, CPS is scalable with respect to density and network size.

Flooding protocols for wireless networks in general have been shown to be very inefficient and therefore are mainly used in network initialization or route discovery and maintenance. In this paper, we propose a framework of constrained flooding protocols. The framework incorporates a reinforcement learning kernel, a differential delay mechanism, and a constrained and probabilistic retransmission policy. This type of protocol takes the advantages of robustness from flooding, but maintains energy efficiency by constraining retransmissions. Without the use of any control packets, such a protocol adapts to the specific routing requirements of the task and the dynamic changes of the network. We analyze this framework in simulation using some real-world applications in sensor networks.

In this paper, we consider the behavior of a wireless ad-hoc sensor network for different radio models. By means of simulations, we analyze the performance of three protocols: AODV, DSR, and DSDV considering two radio models TwoRayGround and Shadowing.

In difference with other works, we generalize the type of radio model by allowing the path loss randomnesses to be present in the service environment of the network. We study the perceived Goodput and Depletion of the ad-hoc sensor network and compare the performance of three protocols for different scenarios. The simulation results confirm the fact that the shadowing phenomena, by destroying the regularity of the network, reduce the mean distance among nodes and at the same time increase the interference level and the latency of packet transmission. In particular, we found a maximum relative difference of 70%. On the other hand, for the proactive DSDV routing protocol the energy consumption rate seems to be independent of the radio model, at least for moderate size of the network (256 nodes). Also, we found that the packet delivery ratio of AODV and DSR routing protocols are more stable than DSDV protocol.

As the utilization of sensor networks continue to increase, the importance of security becomes more profound. Many industries depend on sensor networks for critical tasks, and a malicious entity can potentially cause catastrophic damage. We propose a new key exchange trust evaluation for peer-to-peer sensor networks, where part of the network has unconditionally secure key exchange. For a given sensor, the higher the portion of channels with unconditionally secure key exchange the higher the trust value. We give a brief introduction to unconditionally secured key exchange concepts and mention current trust measures in sensor networks. We demonstrate the new key exchange trust measure on a hypothetical sensor network using both wired and wireless communication channels.

We construct high-order low-bit Sigma-Delta $(\mathrm{\Sigma }\mathrm{\Delta })$ quantizers for the vector-valued setting of fusion frames. We prove that these $\mathrm{\Sigma }\mathrm{\Delta }$ quantizers can be stably implemented to quantize fusion frame measurements on subspaces $W_n$ using ${log}_2(\mathrm{\text{dim}}(W_n)+1)$ bits per measurement. Signal reconstruction is performed using a version of Sobolev duals for fusion frames, and numerical experiments are given to validate the overall performance.

In a distributed system, it is often necessary for nodes to agree on a particular event or to coordinate their activities. Applications of distributed agreement are many, such as Commit Protocols in distributed database systems, selection of a monitor node in a distributed system, detecting an intruder, or agreeing on the malicious behavior of a node.

Among many forms of Distributed Agreement, one form is called Approximate Agreement (AA), in which the nodes, by exchanging their local values with other nodes, need to agree on values which are approximately equal to each other. Research on AA for fully connected networks is relatively mature. In contrast, the study of AA in partially connected networks has been very limited. More specifically, no general solution to the AA problem exists for such networks. This research solves the AA problem for a specific, scalable, partially connected network with limited relays. The research considers the worst failure mode of nodes, called Byzantine, and hybrid failure modes. The results show low communication cost in comparison to fully connected networks. The network is designed to take advantage of the results available for fully connected networks. Thus, the analysis for obtaining the expressions for Convergence Rate and Fault Tolerance becomes relatively easy.

In order to reduce the positioning error of wireless sensor network nodes and deal with the positioning of a large number of sensor network nodes, a mass sensor node positioning method based on particle swarm optimization (SNPSO) is proposed. The node location error of node distance correction value is corrected by SNPSO algorithm, and the sensing data is encoded by the index of cloud computing resources. The dynamic target strategy (DTS) algorithm is used to solve the strict deadline constraints. The algorithm focuses on optimizing execution time to meet deadline constraints, and once feasible solutions are obtained, it focuses on optimizing execution costs within deadline constraints. The performance of the algorithm is simulated and analyzed on the platform of MATLAB 2016. Compared with the sensor positioning method, SNPSO improves the positioning accuracy of mass sensor nodes. The simulation results verify the validity of SNPSO. Compared with the improved quantum genetic algorithm (IQGA) under different scale data scheduling and different deadlines constraints, the proposed algorithm can improve the positioning accuracy of mass sensor nodes. The proposed algorithm can find the optimal solution of cloud computing resource scheduling with lower execution cost under strict deadline constraints, and can more easily meet the needs of massive sensor network node location data processing.

We develop a novel energy-efficient routing protocol called the THEEM (Two-Hop Energy-Efficient Mesh) protocol for wireless sensor networks. In the THEEM protocol, a comprehensive and integrated treatment is employed to achieve energy efficiency. In specific, a two-hop in-mesh transmission scheme and a centralized mesh (cluster) formulation method are employed, along with other design innovations, such as the concepts of mesh layer/column, the power-aware assignment of mesh heads and a low-energy media access protocol. Simulation results show that the THEEM protocol is able to reduce energy consumption quite significantly compared to currently existing protocols. Equal energy dissipation among all sensor nodes in a network is also achieved. In addition, the protocol maximizes network data throughput.

Security in sensor networks is a major issue. Sensor networks use symmetric cryptography protocol since sensor nodes have resource constrained hardware. Such netowrks are also deployed in hostile environments. Therefore, an attacker can get all information after any nodes get compromised. The adversary can inject false sensing reports or false Message Authentication Codes into real reports. A probabilistic voting-based filtering scheme is proposed but in several cases it is inefficient in terms of energy consumption and filtering effectiveness. We proposed a new method that uses a decision function regardless of whether each forwarding node executes a verification process. Through performance analysis and simulation, our result shows that the proposed method is much more efficient than the probabilistic voting-based scheme in many cases.

Genetic programming (GP) is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In most cases it is a hardwired module of a design framework assisting the engineer in optimizing specific aspects in system development. In this article, we show how the utility of GP can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our GP framework produces XMI-encoded UML models that can easily be loaded into widely available modeling tools, which in turn offer code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how GP can be combined with model-driven development (MDD).

In sensor networks, it is an important task to periodically collect data from an area of interest for time-sensitive applications. The sensed data must be gathered and transmitted to a base station for further processing to meet the end-user queries. Since the network consists of low-cost nodes with limited battery power, it is a challenging task to design an efficient routing scheme that can minimize delay and offer good performance in energy efficiency, and long network lifetimes. In this paper, we propose a distance based multi-clustering in sensor networks using vertex subset degree preserving minimum spanning tree.

Efficient use of the network’s resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network’s success in tracking targets, efficiently and effectively, hinges significantly on the network’s ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation; (ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semi-flocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multi-sensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.

Wireless sensor networks have specifically unique characteristics with regards to energy, memory, computational and complexity limitations. This paper shares the experiences gained in designing and implementing a wireless sensor network when we take these considerations into account. We also highlight some challenges faced in the implementation of the network. The hardware that is developed provides a realistic and flexible platform for future implementation and testing of relevant higher layer schemes.