On the problem of k-coverage in mission-oriented mobile wireless sensor networks

The problem of sensor deployment to achieve k-coverage of a field, where every point is covered by at least k sensors, is very critical in the design of energy-efficient wireless sensor networks (WSNs). It becomes more challenging in mission-oriented WSNs, where sensors have to move in order to k-cover a region of interest in the field. In this type of network, there are multiple missions (or monitoring tasks) to be accomplished, each of which has different requirements, particularly, in terms of coverage. In this paper, we consider the problem of k-coverage in mission-oriented mobile WSNs which we divide into two sub-problems, namely sensor placement and sensor selection. The sensor placement problem is to identify a subset of sensors and their locations in a region of interest so it is k-covered with a small number of sensors. The sensor selection problem is to determine which sensors should move to the above-computed locations in the region while minimizing the total energy consumption due to sensor mobility and communication. Specifically, we propose centralized and distributed approaches to solve the k-coverage problem in mission-oriented mobile WSNs. Our solution to the sensor placement problem is based on Helly’s Theorem and the geometric analysis of the Reuleaux triangle. First, we consider a deterministic (or disk) sensing model, where the sensing range is modeled as a disk. Then, based on the above analysis, we address the k-coverage problem using a more realistic sensing model, known as probabilistic sensing model. The latter reflects the stochastic nature of the characteristics of the sensors, namely sensing and communication ranges. Our centralized and distributed protocols enable the sensors to move toward a region of interest and k-cover it with a small number of sensors. Our experiments show a good match between simulation and analytical results. In particular, simulation results show that our solution to the k-coverage problem in mission-oriented mobile WSNs outperforms an existing one in terms of the number of sensors needed to k-cover a region of interest in the field and their total energy consumption due to communication, sensing, and mobility for the correct operation of the protocol.     

An analytical hierarchy process-based approach to solve the multi-objective multiple traveling salesman problem

We consider the problem of assigning a team of autonomous robots to target locations in the context of a disaster management scenario while optimizing several objectives. This problem can be cast as a multiple traveling salesman problem, where several robots must visit designated locations. This paper provides an analytical hierarchy process (AHP)-based approach to this problem, while minimizing three objectives: the total traveled distance, the maximum tour, and the deviation rate. The AHP-based approach involves three phases. In the first phase, we use the AHP process to define a specific weight for each objective. The second phase consists in allocating the available targets, wherein we define and use three approaches: market-based, robot and task mean allocation-based, and balanced-based. Finally, the third phase involves the improvement in the solutions generated in the second phase. To validate the efficiency of the AHP-based approach, we used MATLAB to conduct an extensive comparative simulation study with other algorithms reported in the literature. The performance comparison of the three approaches shows a gap between the market-based approach and the other two approaches of up to 30%. Further, the results show that the AHP-based approach provides a better balance between the objectives, as compared to other state-of-the-art approaches. In particular, we observed an improvement in the total traveled distance when using the AHP-based approach in comparison with the distance traveled when using a clustering-based approach.     

A new scalable multicast routing algorithm for interactive real-time applications

Quality of service (QoS) provisioning generally assumes more than one QoS measure that implies that QoS routing can be categorized as an instance of routing subject to multiple constraints: delay jitter, bandwidth, cost, etc. We study the problem of constructing multicast trees to meet the QoS requirements of real-time interactive applications where it is necessary to provide bounded delays and bounded delay variation among the source and all destinations while keeping overall cost of the multicast tree low. The main contribution of our work is a new strategy for constructing multiconstrained multicast trees. We first derive mathematically a new delay-variation estimation scheme and prove its efficiency. Thereafter, we propose a simple and competitive (in terms of running time) heuristic algorithm, for delay and delay variation constrained routing problem based on the proposed delay-variation estimation scheme and using the Extended Prim-Dijkstra tradeoffs’ algorithm. Our contribution also extends previous works in providing some properties and analyses of delay bounded paths satisfying delay variation constraints. Extensive simulation results show that our algorithm outperforms DVDMR in terms of multicast delay variation with the same time complexity as DVDMR.     

Stick-slip motion control based on cutter location data for an orthogonal-type robot

In this article, a new desktop orthogonal-type robot, which has the capacity of stick-slip motion control based on cutter location data, is presented for lapping small metallic molds with a curved surface. The robot consists of three single-axis devices with a high position resolution of 1 μm. A thin wooden stick tool with a ball-end shape is attached to the tip of the z-axis. In order to improve the lapping performance, a novel stick-slip motion control method is developed in the control system. The small stick-slip motion is orthogonally generated in the direction of the tool’s movement. The effectiveness of stick-slip motion control is examined through an actual lapping test of an LED lens cavity.     

Design of RF MEMS Based Oscillatory Neural Network for Ultra High Speed Associative Memories

Microelectromechanical systems are utilized alongside with transistor amplifiers and resistive connections for implementing of oscillatory associative memories. Phase locking is studied in such a network and all requirements of the circuit level implementation are satisfied. A very high gain trans-impedance amplifier operating in 1 GHz in addition to a novel automatic amplitude control circuit is employed to remove amplitude dynamics of the system. Requiring resonator characteristics are extracted and calculated as well. A new method for initialization of the network is proposed. Each neuron consumes 1.08 mW from a 1.8 V power supply. The convergence time of a typical network trained by Hebbian rule is less than 1.5 ns which results in an ultra high speed analog signal processing system.     

On the energy-delay trade-off in geographic forwarding in always-on wireless sensor networks: A multi-objective optimization problem

The design and development of multi-hop wireless sensor networks are guided by the specific requirements of their corresponding sensing applications. These requirements can be associated with certain well-defined qualitative and/or quantitative performance metrics, which are application-dependent. The main function of this type of network is to monitor a field of interest using the sensing capability of the sensors, collect the corresponding sensed data, and forward it to a data gathering point, also known as sink. Thus, the longevity of wireless sensor networks requires that the load of data forwarding be balanced among all the sensor nodes so they deplete their battery power (or energy) slowly and uniformly. However, some sensing applications are time-critical in nature. Hence, they should satisfy strict delay constraints so the sink can receive the sensed data originated from the sensors within a specified time bound. Thus, to account for all of these various sensing applications, appropriate data forwarding protocols should be designed to achieve some or all of the following three major goals, namely minimum energy consumption, uniform battery power depletion, and minimum delay. To this end, it is necessary to jointly consider these three goals by formulating a multi-objective optimization problem and solving it. In this paper, we propose a data forwarding protocol that trades off these three goals via slicing the communication range of the sensors into concentric circular bands. In particular, we discuss an approach, called weighted scale-uniform-unit sum, which is used by the source sensors to solve this multi-objective optimization problem. Our proposed data forwarding protocol, called Trade-off Energy with Delay (TED), makes use of our solution to this multi-objective optimization problem in order to find a “best” trade-off of minimum energy consumption, uniform battery power depletion, and minimum delay. Then, we present and discuss several numerical results to show the effectiveness of TED. Moreover, we show how to relax several widely used assumptions in order to enhance the practicality of our TED protocol, and extend it to real-world network scenarios. Finally, we evaluate the performance of TED through extensive simulations. We find that TED is near optimal with respect to the energy × delay metric. This simulation study is an essential step to gain more insight into TED before implementing it using a sensor test-bed.     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.