Industry 4.0 utilizes the Internet of Things (IoT) to rise the efficiency in manufacturing and automation where wireless sensor networks (WSNs) are crucial technologies for communication layer of IoT. WSNs include hundreds of small sized sensor nodes that have the abilities of wireless transmission and environmental sensing. Wireless transmission is prone to various attacks such as data manipulation since data communication is achieved through transfer of radio packets. A countermeasure of this issue is link monitoring by deploying secure points that can physically capture and inspect radio packets. Graph theory plays a critical role to solve various problems in WSNs. Finding minimum Vertex Cover (VC) is an important NP-Hard graph theoretic problem in which the minimum set of nodes (vertices) is aimed to select in such a way that each link should be incident to at least one node from this set. VC is a significant structure for WSNs where it perfectly fits for link monitoring when nodes in VC are set as secure points (monitors). Since sensor nodes are generally battery-powered and have limited transmission range, energy-efficient multi-hop communication to the sink node is of utmost importance. In weighted connected VC (WCVC) structure, subgraph induced by monitor nodes are connected where monitors are chosen according to their weights. When weights of nodes are assigned as reciprocal of their energies, an energy-efficient virtual backbone can be formed. We propose a novel metaheuristic WCVC algorithm for link monitoring and backbone formation in WSNs modeled as undirected graphs. Our proposed algorithm integrates a genetic search with a greedy heuristic to improve WCVC solution quality and decrease the search time. To evaluate the efficiencies of greedy heuristics, we adopt three different heuristics for WCVC problem. We implement our algorithm with its counterparts and reveal that the algorithm is favorable in terms of solution quality and resource consumption. INDEX TERMS Internet of things, wireless sensor networks, link monitoring, vertex cover, metaheuristic algorithm.

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A Wireless Sensor Network (WSN) is connected if a communication path exists among each pair of sensor nodes (motes). Maintaining reliable connectivity in WSNs is a complicated task, since any failure in the nodes can cause the data transmission paths to break. In a k-connected WSN, the connectivity survives after failure in any k-1 nodes; hence, preserving the k-connectivity ensures that the WSN can permit k-1 node failures without wasting the connectivity. Higher k values will increase the reliability of a WSN against node failures. We propose a simple and efficient algorithm (PINC) to accomplish movement-based k-connectivity restoration that divides the nodes into the critical, which are the nodes whose failure reduces k, and non-critical groups. The PINC algorithm pickups and moves the non-critical nodes when a critical node stops working. This algorithm moves a non-critical node with minimum movement cost to the position of the failed mote. The measurements obtained from the testbed of real IRIS motes and Kobuki robots, along with extensive simulations, revealed that the PINC restores the k-connectivity by generating optimum movements faster than its competitors.

Internet of things (IoT) envisions a network of billions of devices having various hardware and software capabilities communicating through internet infrastructure to achieve common goals. Wireless sensor networks (WSNs) having hundreds or even thousands of sensor nodes are positioned at the communication layer of IoT. In this study, the authors work on the connectivity estimation approaches for IoT-enabled WSNs. They describe the main ideas and explain the operations of connectivity estimation algorithms in this chapter. They categorize the studied algorithms into two divisions as 1-connectivity estimation algorithms (special case for k=1) and k-connectivity estimation algorithms (the generalized version of the connectivity estimation problem). Within the scope of 1-connectivity estimation algorithms, they dissect the exact algorithms for bridge and cut vertex detection. They investigate various algorithmic ideas for k connectivity estimation approaches by illustrating their operations on sample networks. They also discuss possible future studies related to the connectivity estimation problem in IoT.

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Monitoring the links of wireless sensor networks (WSNs) is a very crucial operation to detect security attacks targeted to the legitimate nodes in internet of things. To achieve this, identifying the monitor nodes (secure points) among the nodes in a WSN and assigning their links are of utmost importance. Vertex cover is a popular problem in the areas of graph theory, approximation algorithms and optimization. Vertex cover is a set of nodes where an edge (link) is incident to at least one of nodes in this set. Hence, vertex cover can be used as the set of the monitor nodes. Capacitated vertex cover, which restricts the edge count that a node can cover, is a specialized version of the vertex cover problem. It provides energy-efficient link monitoring by restricting the link count. In this study, we evaluate capacitated vertex cover algorithms in terms of the cardinality of vertex cover, running time, and approximation ratio. To the best of our knowledge, this is the first evaluation in this manner. Firstly, we theoretically analyze the capacitated vertex cover algorithms, then implement these algorithms in SageMath language that is utilized for solving linear programming and mathematical problems. From our obtained extensive measurement results, we reveal that Naor 8-approximation algorithm performs best having the lowest approximation ratio with 1.13 by selecting 1.12 times fewer vertices in the feasible execution time, although its approximation ratio is worse than the others.

Internet of Things (IoT) envisions the connection of billions of devices over the Internet. The data produced by these huge amount of devices grow exponentially, so analyzing this big data with traditional methods is not viable. Recent cloud computing and virtualization technologies cope with these issues by processing and storing IoT data. Wireless sensor networks (WSNs) are big data sources of IoT systems which provides data collection from the environment. WSNs are used in various applications such as habitat monitoring, military surveillance and smart agriculture. Data transmission to the sink node is one of the essential requirements for WSNs. Clustering is a fundamental technique that is used for efficient data transmission, time synchronizaion, load balancing and security services. In this paper, we propose a clustering framework that we call BICOT for WSNs tailored for IoT systems. BICOT inputs large scale node position, transmission range and node energy data and outputs clustering information. Our first algorithm (BICOT-CDS) is based on connected dominating set (CDS) structure and aims to reduce the cluster count. Our second algorithm uses a weighted CDS (WCDS) approach that targets to select nodes with high energy as cluster heads. We implement these algorithms in ns2 simulator environment and measure cluster count and total weight of cluster head values. The algorithms are tested against node counts and average node degrees. From extensive simulation measurements, we obtain that the cluster count generated by BICOT-CDS is far more better than its counterparts and as the network size increases the proposed algorithm performs better. The cost of dominators produced by the BICOT-WCDS algorithm is significantly lower than its competitors. These findings show us that our proposed algorithms are favorable big data analysis approaches for cloud based IoT systems.