Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks

We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors. Since the related min-max robustness Integer Linear Programming (ILP) problem can be difficult to solve even for state-of-the-art commercial optimization solvers, we propose an original heuristic for its solution. The heuristic combines deterministic and probabilistic variable fixing strategies, guided by the information coming from strengthened linear relaxations of the ILP robust model, and includes a very large neighborhood search for reparation and improvement of generated solutions, formulated as an ILP problem solved exactly. Computational tests on realistic instances show that our heuristic finds solutions of much higher quality than a state-of-the-art solver and than an effective benchmark heuristic.Comment: This is the authors' final version of the paper published in G. Squillero and K. Sim (Eds.): EvoApplications 2017, Part I, LNCS 10199, pp. 1-17, 2017. DOI: 10.1007/978-3-319-55849-3\_16. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55849-3_1

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page

Towards Efficient Sensor Placement for Industrial Wireless Sensor Network

Industrial Wireless Sensor Network (IWSN) is the recent emergence in wireless technologies that facilitate industrial applications. IWSN constructs a reliable and self-responding industrial system using interconnected intelligent sensors. These sensors continuously monitor and analyze the industrial process to evoke its best performance. Since the sensors are resource-constrained and communicate wirelessly, the excess sensor placement utilizes more energy and also affects the environment. Thus, sensors need to use efficiently to minimize their network traffic and energy utilization. In this paper, we proposed a vertex coloring based optimal sensor placement to determine the minimal sensor requirement for an efficient network

Optimal Service Provisioning in IoT Fog-based Environment for QoS-aware Delay-sensitive Application

This paper addresses the escalating challenges posed by the ever-increasing data volume, velocity, and the demand for low-latency applications, driven by the proliferation of smart devices and Internet of Things (IoT) applications. To mitigate service delay and enhance Quality of Service (QoS), we introduce a hybrid optimization of Particle Swarm (PSO) and Chemical Reaction (CRO) to improve service delay in FogPlan, an offline framework that prioritizes QoS and enables dynamic fog service deployment. The method optimizes fog service allocation based on incoming traffic to each fog node, formulating it as an Integer Non-Linear Programming (INLP) problem, considering various service attributes and costs. Our proposed algorithm aims to minimize service delay and QoS degradation. The evaluation using real MAWI Working Group traffic data demonstrates a substantial 29.34% reduction in service delay, a 66.02% decrease in service costs, and a noteworthy 50.15% reduction in delay violations compared to the FogPlan framework

Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks

أصبحت شبكات الاتصالات المحمولة في الوقت الحاضر جزءًا متأصلاً في حياتنا اليومية من خلال الكميات الهائلة من البيانات التي يتم تناقلها عبر أجهزة الاتصال، مما يقود إلى تحديات جديدة. وسيتم حسب مؤشر سيسكو للشبكات، توصيل أكثر من 29.3 بليون جهاز عبر  الشبكة خلال العام 2023.من الواضح أن البنى التحتية الموجودة في الشبكات الحالية لن تكون قادرة على دعم جميع البيانات التي يتم تبادلها بسبب عرض الحزمة المحدود وكلفة عمليات الإرسال والمعالجة. ومن أجل التعامل مع هذه المشكلات، يجب أن تحقق شبكات الاتصالات المحمولة المستقبلية متطلبات عالية من أجل إنقاص كمية البيانات المنقولة وتقليل زمن الوصول وكلفة عمليات المعالجة. تتمثّل إحدى التحديّاتِ العلميّةِ الهامّة ضمن هذا السياق في التوضيع المثالي للخدماتِ ذاتيّة التّأقلمِ.تمّ في هذه الورقة البحثية تقديم خوارزمية استدلالية لتوضيع الخدمات في الشبكات المستقبلية. تحقق هذه الخوارزميّة التوضيع المثالي لنسخ الخدمات من خلال مراقبة الحمل داخل عقدة المخدم وجوارها، واختيار العقدة التي يتمّ تلقّي الحمل الأكبر منها، ونسخ الخدمة أو تهجيرها إليها بناءً على معايير محددة، فتصبح بالتالي المسافة التي تعبرها الطلبات الواردة من العقد الزبائن صغيرة قدر الإمكان بسبب توضيع الخدمات في مواقع قريبة منها. تمّ الإثبات أنّ الخوارزميّة المقترحة من قبلنا تحقّق أداءً محسّنًا من ناحية تلبية الخدمات خلال زمن أقصر، وعرض حزمة أصغر وبالتالي كلفة اتصال أقلّ. أُجريت مقارنة بين هذه الخوارزمية وكل من نموذج الزبون-مخدّم التقليدي وخوارزميّة التوضيع العشوائي. أثبتت النتائج التجريبية أنّ الخوارزميّة الاستدلاليّة تتفوّق على الطرق الأخرى وتحقّق الأداء الأمثل من أجل شبكاتٍ بأحجامٍ مختلفة وسيناريوهات بأحمالٍ متنوعة.Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject area is the optimal self-organized service placement. In this paper a heuristic-based algorithm for service placement in future networks was presented. This algorithm achieves the ideal placement of services replicas by monitoring the load within the server and its neighborhood, choosing the node that contributes with the highest received load, and finally replicating or migrating the service to it based on specific criteria, so that the distance of requests coming from clients becomes as small as possible because of placing services within nearby locations. It was proved that our proposed algorithm achieves an improved performance by meeting the services within a shorter time, a smaller bandwidth, and thus a lower communication cost. It was compared with the traditional client-server approach and the random placement algorithm. Experimental results showed that the heuristic algorithm outperforms other approaches and meets the optimal performance with different network sizes and varying load scenarios