Nanodevices , which are only a few nanometers (nm) in size, are interconnected to form the Internet of Nano-Things (IoNT) that performs complex operations. One of the key challenges is ensuring efficient channel access control for nanodevices, especially when dealing with large network sizes. Medium access control (MAC) protocols serve this purpose, but traditional approaches are not practical due to the inherent constraints of nanodevices. In this paper, we propose two novel MAC protocols for IoNT applications. The first protocol, Slot Assignment-Based (SAB) MAC, is a contention-free method relying on scheduling. In contrast to its counterparts, it enables simultaneous packet transmission through Time Spread On-Off Keying (TS-OOK), effectively minimizing the collision probability and end-to-end delay. The second protocol, Receiver-Initiated and Directed (RID) MAC, adopts a contention-based approach to reduce unnecessary transmissions caused by flooding . It achieves this by limiting the number of active nanodevices within a time interval using directional antennas without incurring scheduling overhead. We evaluated the performance of these protocols through comprehensive simulations, comparing them with counterparts in terms of packet transmission success, energy consumption, end-to-end delay and setup overhead. In dense topologies, SAB-MAC outperforms Transparent (TRN) MAC by approximately twice the packet transmission success reaching up to 95.73%. It accomplishes this with 1000 times lower end-to-end delay and reduced setup overhead than Time Division Multiple Access (TDMA). Conversely, RID-MAC achieves twice the packet transmission success of TRN-MAC and ten times that of unicast-based methods, all with lower end-to-end delay and nearly equivalent energy consumption. Consequently, due to its superior performance SAB-MAC is the optimal choice for communication between nanorouters (NRs). However, RID-MAC is more suitable for communication between nanosensors (NSs), as it incurs no setup overhead.

Drones, particularly when deployed in swarms, hold immense potential for various applications, such as aerial imaging, delivery services, disaster response, and advanced surveillance. Their effective and efficient use, however, hinges on the accurate estimation of energy consumption. This study focuses on determining energy consumption patterns for small drones weighing less than 2 kg and with diameters under 1 m. Through an extensive series of test flights, empirical energy consumption data was collected and used to develop four distinct energy models: a theoretical model based on fundamental flight forces, a simple linear regression model, a cubic polynomial regression model, and a machine learning-based model using the XGBoost Regressor algorithm. A cost function for swarm topology control was also derived for assessing energy consumption during various activities, like connectivity restoration and formation change, facilitating more energy-efficient decision-making in swarm operations. Our findings highlighted an energy-efficient “valley” within the airspeed range, indicating that flying at speeds outside this range results in higher energy consumption. The machine learning model demonstrated superior accuracy within its training range, achieving an R$^2$of 0.9999, whereas the polynomial regression model was deemed best for extrapolation purposes, delivering an R$^2$of 0.966. Simple linear regression and theoretical models, although less accurate, can offer quick energy demand calculations and insights into the effects of hardware modifications, respectively.

Maintenance of connectivity in mobile ad hoc networks (MANETs) and especially in flying ad hoc networks, consisting of unmanned aerial vehicles (UAV), has crucial importance. Missions planned within these types of networks can be interrupted due to node failures, link errors, etc. This case becomes more critical when the application is heavily communication-dependent. To alleviate such problems, in this article, we propose a distributed proactive$k$-connectivity recovery algorithm (DP$k$CR) for UAV-based MANETs. To this end, the algorithm has been developed and tested by providing realistic scenarios. The time and message complexity analysis of the algorithm is presented. Moreover, to analyze the performance of the proposed algorithm, we compared it with other$k$-connectivity restoration algorithms in the literature. Simulation results revealed that DP$k$CR outperforms the alternatives in terms of convergence time for the recovery phase and, subsequently, in terms of energy consumption. Furthermore, DP$k$CR provides improvements to the bandwidth requirements for the restoration.

Random geometric graphs can be used in constructing topology formations for wireless ad-hoc networks (WANETs), including wireless sensor networks, flying ad-hoc networks, and others. They are useful for energy-saving schemes where a randomly alternating subset of deployed nodes is turned off temporarily (without compromising the network connectivity). This also improves network security by periodically rerouting the data flow, which makes it harder for third parties to run a traffic analysis. In the area and target coverage scenarios, a WANET deployment is desired to have maximal area coverage efficiency, which implies covering the largest possible area using the fewest number of nodes by maintaining the connectivity as well. Although deterministic topology formations offering optimal area coverage are known, a random node deployment method that grants connectivity and area coverage maximality has not been presented, to the best of our knowledge. This study introduces a novel topology formation method, called CoRMAC, that consistently yields tree-formed random geometric graphs that are guaranteed to be connected and offer maximal area coverage for any given area size and node cardinality. The area coverage maximality of CoRMAC is formally proven. Extensive theoretical and computational analyses have been provided to demonstrate its graph-theoretic, Euclidean, and networking features. Moreover, comparisons were made with other approaches to show the effectiveness of the proposed method.

Underwater wireless sensor networks (UWSNs) are utilized for a wide range of monitoring and surveillance applications. Lifetime maximization and maintenance of network reliability are among the most important considerations in the deployment of UWSNs.$k$-connectivity is a robust approach for reinforcing reliability. However, maintaining$k$disjoint paths from each sensor node to the BS, inevitably, results in extra energy dissipation, which reduces the network lifetime (NLT). Yet, there is no systematic exploration to determine the extent of lifetime reduction due to the increase in the$k$value, in the literature, to the best of our knowledge. In this study, we create an optimization framework to be able to explore the tradeoff between NLT and$k$-connectivity-based reliability in UWSNs. Through the optimal solutions of the proposed optimization model for a large set of salient parameters, we characterize the interplay between lifetime and$k$-connectivity. Our analysis reveals that the$k$value to be maintained in a UWSN can affect the NLT significantly.

Mobile sensor networks (MSNs) have emerged from the interaction between mobile robotics and wireless sensor networks. MSNs can be deployed in harsh environments, where failures in some nodes can partition MSNs into disconnected network segments or reduce the coverage area. A$k$-connected network can tolerate at least$k$-1 arbitrary node failures without losing its connectivity. In this study, we present a coverage-aware distributed$k$-connectivity maintenance (restoration) algorithm that generates minimum-cost movements of active nodes after a node failure to preserve a persistent$k$value subject to a coverage conservation criterion. The algorithm accepts a coverage conservation ratio (as a trade-off parameter between coverage and movements) and facilitates coverage with the generated movements according to this value. Extensive simulations and testbed experiments reveal that the proposed algorithm restores$k$-connectivity more efficiently than the existing restoration algorithms. Furthermore, our algorithm can be utilized to maintain$k$-connectivity without sacrificing the coverage, significantly.

For many wireless ad-hoc network (WANET) applications, including wireless sensor, robotic, and flying ad-hoc networks, area coverage is a major challenge. This challenge, which may include the number of required nodes, cumulative energy consumption, or total distance travelled, involves covering the largest possible area with the least cost. Whatever the scenario and cost functions, efficiently deploying the nodes throughout their entire missions is an important factor. For larger networks, this can best be performed using proper topology formations that are essentially designated geometric graph patterns. As a network topology formation and node deployment strategy, this study presents a unique tree-formed geometric graph pattern. With any given number of nodes, it guarantees the maximum possible combined area coverage. Considering its graph theory and Euclidean characteristics, the pattern is then described as both algebraic and algorithmic relations. The formation is remarkably scalable because it may include an unlimited number of nodes while ensuring connectivity in a tree topology. In addition to presenting a breakdown of multiple features and attributes, including symmetry and fractality, we will make a comparative analysis with six known regular lattices and a line formation. Our analyses then show that MAX-Tree covers up to twice as much area compared to all other regular lattices of the same node cardinalities while being more robust and reliable than a line formation. MAX-Tree can be extremely beneficial for drone networks, smart industry and agriculture applications, ocean-bed monitoring systems, and many other WANET scenarios.

Connectivity maintenance is an important requirement in wireless sensor and actuator networks (WSANs) because node failures can, potentially, lead to destructive changes in the network topology, which, in turn, can create a partitioned network. Preserving k-connectivity in a WSAN is important for keeping stable connections. A k-connected network is a network that remains connected after removing any k-1 nodes. Higher k values provide more reliable connectivity and a higher level of fault tolerance. In this article, we present a distributed k-connectivity restoration approach for heterogeneous WSANs where the nodes can be static or mobile. In the proposed algorithm, each node identifies the mobile nodes in the network and its 2-hop local subgraph. After a node is incapacitated, a neighbor of the failed node calls a mobile node with minimum moving cost to the location of the failed node if the failure reduces k. A minimum cost movement path between a neighbor of the failed node and a mobile node is constructed by considering the locations of the nodes, moving costs, and obstacles. Testbed experiments and comprehensive simulations reveal that the proposed distributed algorithm is capable of restoring k-connectivity with up to 35.5% lower sent Bytes and up to 40.9% lower movement cost than the existing algorithms.

Wireless ad hoc and sensor networks, one of the important components of the Internet of Things, consist of hundreds or even thousands of tiny sensor nodes without a fixed infrastructure. Due to lack of transmission power, the presence of obstacles, and other environmental conditions, ordinary sensor nodes that are powered from their batteries may have to send data in a multi-hop manner to the sink node. Therefore, it is very important to conduct an energy-efficient and fault-tolerant routing algorithm. Clustering is one of the most popular methods for routing in wireless sensor networks. With the help of clustering, nodes are classified as cluster leaders and ordinary nodes. In this study, a distributed, fault-tolerant cluster leader selection algorithm is proposed for a wireless sensor and ad hoc networks that can be modeled as a unit disk graph. The proposed algorithm is self-stabilized and it is based on a maximum independent set. The theoretical analysis of the proposed algorithm is performed, it is implemented in SimPy environment and compared with other algorithms. According to the measurements taken, the proposed algorithm performs significantly better than its counterparts.

Maximal independent set (MIS) has significantly important in practical applications for wireless sensor networks (WSNs). A distributed self-stabilizing system can initially start at any illegal state and takes back a legal state as long as there is no external intervention. We propose a novel distributed self-stabilizing MIS algorithm using two-hop information. It stabilizes an unstable system at most n-1 moves under an unfair distributed scheduler where n is the number of nodes in the graph. We use the message passing model as the communication model where this model is very appropriate for WSNs. The communication consumes the most energy in WSNs. So, move count is at least as important as round count where reducing the move count prolongs the lifetime of the network. We analyzed theoretically and tested it on SimPy discrete event simulator on randomly generated connected simple undirected graphs to compare with its counterparts in terms of transmitted bit count and move count against various node degrees and node counts.

In Wireless Sensor Networks (WSNs) keeping the network connectivity is a challenging task because failure in some nodes may cut off the communication paths between other nodes. A k-connected network remains connected after failure in any k-1 nodes, hence we can consider the k value as a metric for measuring the connectivity robustness of WSNs. In this paper we consider the effect of random node distribution and transmission range of nodes on k value of WSNs. To evaluate the effect of node count and transmission range on k, we generated 1000 random topologies with different transmission range and node count and measured the k value of established networks. Our simulation result showed that in a field of 1000 × 1000 m2area, with unified random distribution we need at least 200 nodes with minimum transmission range 80 m to expect a network with k ≥ 1. Also, the simulation results showed that random distributing of up to 500 nodes with transmission range lower than 80 in a field with mentioned area, generally leads to disconnected networks.

Compliance monitoring and surveillance is an important task in hospitals and health centers as the health-care associated infections can have serious consequences such as increasing mortality and accelerating morbidity. Monitoring and surveillance by human observers have its own difficulties such as loss of observer concentration, human error build-up, and professional deformation. In this work, we have implemented and compared the performance of two Internet of Things based approaches for monitoring the hand hygiene of medical staff during patient visiting. We used ESP modules as base stations and smart-phones as mobile nodes and estimated the distances using Bluetooth RSSI values to locate the medical staff in a patient's room. In the proximity-based solution, we compared the RSSI from a mobile node measured on different ESP nodes/modules and utilized the assumption stating that the mobile node is closest to an ESP node which yields the highest RSSI value. In the trilateration based approach, we used the RSSI values to estimate the distance of mobile nodes to each ESP node and we used a trilateration algorithm to locate the mobile node in the room. Our experiments showed that the proximity-based solution recorded 20% incorrect location visiting while the percentage for the trilateration based solution was 8%. This indicates that the trilateration based approach is more reliable than the proximity-based solution in this application domain.

A critical node (cut vertex or articulation point) in wireless sensor networks, is a node which its failure breaks the connectivity of the network. Therefore, it is crucial that critical nodes be detected and treated with caution. This paper provides two localized distributed algorithms for determining the states of nodes (critical or noncritical). The first proposed algorithm identifies most of the critical and noncritical dominator nodes from two-hop local subgraph and connected dominating set (CDS) information that limits the computational complexity to O(Δ2) and bit complexity to O(clog2n) where Δ is the maximum node degree, c is the critical node count, and n is the node count. The testbed experiments and simulation results show that this algorithm detects up to 93% of critical nodes and achieves up to 91% of state determination with low energy consumption. The second proposed algorithm, which is based on the first one, finds the states of all nodes by running a limited distributed depth-first search algorithm in unrecognized parts of the network without traversing the whole network. Comprehensive testbed experiments and simulation results reveal that, in the presence of a CDS, this algorithm finds all critical nodes with lower energy consumption than all existing algorithms.

Maintaining connectivity is a very important objective of wireless sensor networks (WSNs) in successfully achieving data collection for applications. A cut vertex (node) is defined as a critical vertex whose removal disconnects a network component and partially disables data delivery. Hence, it is crucial that cut vertices be detected and treated with caution. In this paper, we propose an energy-efficient cut vertex detection (CVD) algorithm for WSNs. Our algorithm uses a depth-first search approach and is completely distributed. It benefits from the radio multicast capabilities of sensor nodes and is the first algorithm with a time complexity of O(N) and a sent message complexity of O(N), in which each message is O(log2(N)) bits. We show the operation of the algorithm, analyze it in detail, provide testbed experiments and extensive simulations. We compare our proposed algorithm with the other CVD algorithms and show that our algorithm saves up to 6.8 times more energy in less time.

In wireless ad hoc and sensor networks, energy is a scarce resource and a considerable amount of energy is dissipated due to interference. Therefore, interference is one of the major challenges in wireless ad hoc and sensor networks. It alters or disrupts a message as it is being transmitted along a channel between source and destination. Since the messages are disrupted when the interference occurs, they have to be detected and the interfered messages have to be retransmitted. In this paper, we propose central and distributed heuristic algorithms for reducing average interference in a receiver-centric interference model. In the literature, the minimum spanning tree (MST) algorithm is generally used through the interference coverage graph directly or indirectly in order to generate minimum average interference topology. Our algorithm, dynamic average interference (DAI), however, generates lower average interference as well as more sparse topology than MST. We realized that if the transmission ranges of nodes are taken into consideration at each stage of the topology control algorithm, the interference of links are changed dynamically. This interference changing enables up to 22% more energy saving than the MST algorithm. Thus, DAI provides energy saving by reducing the interference as far as possible in generated topology.

We propose a two layer protocol for tracking fast targets in sensor networks. At the lower layer, the Distributed Spanning Tree Algorithm (DSTA) [12] partitions the network into clusters with controllable diameter and constructs a spanning tree backbone of cluster heads rooted at the sink. At the upper layer, we propose a target tracking algorithm which wakes clusters of nodes by using the estimated trajectory beforehand, which is different from existing studies [3] in which target can be detected only when the nodes close to the target are awake. We provide the simulation results and show the effect of fore-waking operation by comparing error and miss ratios of existing approaches with our proposed target tracking algorithm.

Clustering is a widely used technique to manage the essential operations such as routing and data aggregation in wireless sensor networks (WSNs). We propose two new graph-theoretic distributed clustering algorithms for WSNs that use a weighted matching method for selecting strong links. To the best of our knowledge, our algorithms are the first attempts that use graph matching for clustering. The first algorithm is divided into rounds; extended weighted matching operation is executed by nodes in each round; thus the clusters are constructed synchronously. The second algorithm is the enhanced version of the first algorithm, which provides not only clustering but also backbone formation in an energy-efficient and asynchronous manner. We show the operation of the algorithms, analyze them, provide the simulation results in an ns2 environment. We compare our proposed algorithms with the other graph-theoretic clustering algorithms and show that our algorithms select strong communication links and create a controllable number of balanced clusters while providing low-energy consumptions. We also discuss possible applications that may use the structure provided by these algorithms and the extensions to the algorithms.