Performance Evaluation and Enhancement of Surface Coverage Relay Protocol

In this paper, a localization technique is used to reduce the number of active sensors at an instant of time in a region for wireless sensor networks(WSNs). The active nodes in a region is calculated baed on the K-hop Independent Dominating Set(K-IDS). The proposed algorithm is evaluated using Castalia Simulator.The simulation results are analysed using network parameters namely,energy consumption,number of active nodes in a cluster and packet delivery ratio. The simulation results indicates that the proposed algorithm outperforms LEACH and MOCA protocls.

For wireless sensor networks, monitoring large inaccessible areas where deterministic node deployment is not possible, self-organized techniques are in demand to cover an area using optimal number of nodes. In this paper, given an initial random deployment of mobile sensor nodes, we propose a simple and novel technique for self-organized node movement to satisfy the coverage of the given region of interest using a least number of nodes, such that the maximum node displacement is minimized. We present a simple centralized algorithm and also a distributed version of it for node placement. Moreover, in case of a node failure, a distributed fault recovery algorithm is proposed to replace it locally utilizing the available free nodes. Analysis, simulation, and comparison studies show that the proposed algorithms with less neighborhood information result in significant improvement in terms of average and maximum displacement of a node, rounds of communication, and number of active nodes.

The ad hoc wireless sensor network is usually deployed in severe environment with limit power supply, whose topology is dynamic and flexible. We developed an access-based energy efficient clustering protocol (ABEE), whose clustering algorithm is based on the outcome of the multiple access to the public channel. ABEE retains the advantage of access-based scheme such as self-organization, flexibility and stability. Also by considering the energy dissipation during the compression and transmission of the collected data, ABEE can provide uniformly distributed and energy efficient infrastructure for the upper lever protocols. We also propose to use coverage to judge the robustness of the network as well as the number of active nodes. Simulation is used to compare ABEE with the exclusively access-based clustering protocol in terms of rationality of the cluster structure, system lifetime and the coverage area. Our result shows that the ABEE can partition the large scale of nodes more rationally, efficaciously improve the system lifetime and the coverage of the whole network.

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks.

In pervasive computing environments, it is often required to cover a certain service area by a given deployment of nodes or access points. In case of large inaccessible areas, often the node deployment is random. In this paper, given a random uniform node distribution over a 2-D region, we propose a simple distributed solution for self-organized node placement to satisfy coverage of the given region of interest using least number of active nodes. We assume that the nodes are identical and each of them covers a circular area. To ensure coverage we tessellate the area with regular hexagons, and attempt to place a node at each vertex and the center of each hexagon termed as target points. By the proposed distributed algorithm, unique nodes are selected to fill up the target points mutually exclusively with limited displacement. Analysis and simulation studies show that proposed algorithm with less neighborhood information and simpler computation solves the coverage problem using minimum number of active nodes, and with minimum displacement in 95 % cases. Also, the process terminates in constant number of rounds only.

Cooperative communication is regarded as a key technology in wireless networks, including cognitive radio networks (CRNs), which increases the diversity order of the signal to combat the unfavorable effects of the fading channels, by allowing distributed terminals to collaborate through sophisticated signal processing. Underlay CRNs have strict interference constraints towards the secondary users (SUs) active in the frequency band of the primary users (PUs), which limits their transmit power and their coverage area. Relay selection offers a potential solution to the challenges faced by underlay networks, by selecting either single best relay or a subset of potential relay set under different design requirements and assumptions. The best relay selection schemes proposed in the literature for amplify-and-forward (AF) based underlay cognitive relay networks have been very well studied in terms of outage probability (OP) and bit error rate (BER), which is deficient in multiple relay selection schemes. The novelty of this work is to study the outage behavior of multiple relay selection in the underlay CRN and derive the closed-form expressions for the OP and BER through cumulative distribution function (CDF) of the SNR received at the destination. The effectiveness of relay subset selection is shown through simulation results.

In this paper, an additional entropy penalty term is used to steer the direction of the hidden node's activation in the process of learning. A state with minimum entropy means that most nodes are operating in the non-linear zones (i.e. saturation zones) near the extreme ends of the Sigmoid curve. As the training proceeds, redundant hidden nodes' activations are pushed towards their extreme value corresponding to a low entropy state with maximum information, while some relevant nodes remain active in the linear zone. As training progresses, more nodes get into saturation zones. The early creation of such nodes may impair generalization performance. To prevent the network from being driven into saturation before it can really learn, an entropy cycle is proposed in this paper to dampen the creation of such inactive nodes in the early stage of training. At the end of training, these inactive nodes can then be eliminated without affecting the performance of the original network. The concept has been successfully applied for pruning in two classification problems. The experiments indicate that redundant nodes are pruned resulting in optimal network topologies.

A holistic sensor network design for energy conservation and efficient data dissemination

Containers are increasingly gaining popularity and are going to be a major deployment model in cloud computing. However, consolidation technique is also used extensively in the cloud context to optimize resources utilization and reduce the power consumption. In this paper, we present a new containers scheduling consolidation approach for cloud computing environment based on a machine learning technique. Our approach is proposed to address the problem of a company that aims to adapt dynamically the number of active nodes to reduce the power consumption when several containers are submitted online each day by their users. In our context, the frequency of containers submission varies within one hour. However, for each hour, the submission frequency is essentially the same each day. The principle of our approach consists into applying a machine learning technique to detect, from a previous containers submission historical, three submission periods (high, medium and low). Each submission period represents a time slot of one day. For instance, the high submission period represents the slot time where the number of submitted containers is the highest compared to other periods. Then, according to the submission periods slot time, our approach dynamically adapts the number of active nodes that must be used to execute each new submitted container. Our proposed consolidation approach is implemented inside Docker Swarmkit which is a well-known container scheduler framework developed by Docker. Experiments demonstrate the potential of our approach under different scenarios.

This paper presents the detection performance of the two-dimensional optical CDMA network under varying conditions of the bit-level design parameters such as the chip length (M), the number of wavelengths in use (L), and the code weight (W). In particular, the code weight that results in the minimal BER is analytically determined as W ≈ 1.4LM/m under the assumption of sparsely distributed random code selection, where m represents the number of active nodes in the network. The derived expression is shown to closely match the optimal code weight obtained via simulation for the cases of practical interest. The BER performance using the optimal code weight when the number of active nodes is not known (and hence estimated) is also presented and compared with the optimal performance.KeywordsActive NodeCode Division Multiple AccessOptimal CodeCode Division Multiple Access SystemLightwave TechnologyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Nanotechnology becomes reality paving the way for many new applications. In nanonetwork system, each nanosized device is equipped with limited capabilities and is dedicated to a basic task but the combination of the numerous devices actions results in high-level functions. In this context, large number of devices concentrated in a limited area must exchange data using wireless links. Spread in Time On-Off Keying (TS-OOK) protocol was proposed as a technique to share the radio channel over the different terahertz nano-devices. TS-OOK is based on a Femtosecond-Long pulse modulation where communication data are sent using a sequence of pulses interleaved by a constant duration randomly selected. In this paper we provide a critical analysis of the TS-OOK approach. We prove that the TS-OOK is not adaptive against the traffic load variation and induces an imbalance between the active nodes. This inequity is due to the dependency of a communication quality on the randomly chosen symbol rate. We propose a dynamic TS-OOK modulation approach, called SRH-TSOOK (Symbol rate Hopping TSOOK), where the duration between two consecutive pulses of the same transmission follows a pseudo-random sequence. We show that this approach performs better than the standard protocol when the number of active nodes increases and guarantees a better distribution of the channel capacity over the active communications. For instance, while the throughput of a communication within a TS-OOK protocol may falls bellow 105 frames/s with 300 active nodes, the throughput in the SRH-TSOOK protocol stabilizes around 207 frames/s for all the active communications. The comparison is made on the basis of probabilistic analysis allowing a numerical and accurate evaluation of the protocols performance.

In this paper, we introduce distributed traffic regulation for wireless local area networks (WLANs) operating in ad hoc mode. We use the concept of service curve to determine the minimum guaranteed service given to a backlogged connection. In a WLAN, the service curve depends on the number of active nodes, their traffic load in active periods, and the back-off procedure used for contention resolution. We will show that service curve can be used for distributed traffic regulation. We use some of the data packets, denoted as the probing packets, to estimate the service curve. The call is accepted if the service curve is higher than a preselected threshold, called the universal lower bound. The universal lower bound is independent of the number of nodes and traffic fluctuation and acts as a worst-case reference point for the network performance.

Wireless Sensor Network WSN is consisted of nodes with different sizes and a specific goal. Tracking applications are very important in WSNs. This study proposes a method for reducing energy consumption in WSNs, considering target tracking. A prediction method is also presented with the help of MAC Medium Access Control layer in which a few number of nodes are in active mode to track the target while the rest of the nodes are switched into sleeping mode. Energy consumption was reduced by decreasing the number of nodes involved in target tracking and activating merely a limited number of necessary nodes. The conducted experiments suggest that the proposed algorithm has a much better performance compared to similar algorithms in different conditions, including energy consumption in different communication ranges, effect of the number of nodes on energy consumption, the number of nodes involved in tracking in terms of communication range, and losing the target. This means the proposed method is superior and more efficient. When the target enters the simulation area, nodes of the network identify the target node through the proposed method (which is the optimum method) and track it as long as it is inside the simulation area. Performance criteria, including energy, other nodes, and throughput were used in simulation. The results showed that the proposed method has the optimum performance and reduces energy consumption of the network during tracking. The results of the simulation showed that the proposed method reduces energy consumption to an acceptable extent, compared to previous methods, in addition to increasing the accuracy of target tracking. Keywords: Active nodes, MAC layer, Energy efficiency, Target tracking, Wireless sensor networks, Target tracking.

During the process that wireless sensor network try to cover its target area, a lot of redundant nodes are generated, resulting in excessive network energy consumption and incomplete node coverage. Against this problem, an efficient covering algorithm based on point set optimization is proposed. In the algorithm, the Gaussian normal density function and coverage area probability function are utilized during optimization of the point set, so that an optimal point set which meets certain coverage requirements is given through the quantitative relationship between the node sensing radius and number of nodes in the network, and furthermore, the network resource is optimized, the lifetime and Qos of the network are improved, and the network overhead is also reduced. Simulation result shows that: during test results against different coverage area and comparison to SCCP algorithm, the effectiveness and stability of the algorithm are verified.

Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protocol, called “mobicast”. In this protocol, it is assumed that the area around the moving target, called the delivery zone, is known at any given time during the operation of the network. The aim of the protocol is to awake sensor nodes, which will be within the delivery zone in the near future, to be prepared for tracking the approaching moving target. In this paper, we propose a novel mobicast algorithm, aiming at reducing the number of awakened sensor nodes. To this end, we equipped every sensor node with a learning automaton, which helps the node in determining the sensor nodes it must awaken. To evaluate the performance of the proposed algorithm, several experiments have been conducted. The results have shown that the proposed algorithm can significantly outperform other existing algorithms such as forward-zone constrained and FAR in terms of energy consumption, number of active nodes, number of exchanged packets and slack time.