Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH) while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

Underwater wireless sensor networks are a newly emerging wireless technology in which small size sensors with limited energy and limited memory and bandwidth are deployed in deep sea water and various monitoring operations like tactical surveillance, environmental monitoring, and data collection are performed through these tiny sensors. Underwater wireless sensor networks are used for the exploration of underwater resources, oceanographic data collection, flood or disaster prevention, tactical surveillance systems, and unmanned underwater vehicles. Sensor nodes consist of a small memory, a central processing unit, and an antenna. Underwater networks are much different from terrestrial sensor networks as radio waves cannot be used in underwater wireless sensor networks. Acoustic channels are used for communication in deep sea water. Acoustic signals have many limitations, such as limited bandwidth, higher end-to-end delay, network path loss, higher propagation delay, and dynamic topology. Usually, these limitations result in higher energy consumption with a smaller number of packets delivered. The main aim nowadays is to operate sensor nodes having a smaller battery for a longer time in the network. This survey has discussed the state-of-the-art localization based and localization-free routing protocols. Routing associated issues in the area of underwater wireless sensor networks have also been discussed.

The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.

Vehicular ad hoc network (VANET) is a wireless emerging technology that aims to provide safety and communication services to drivers and passengers. In VANETs, vehicles communicate with other vehicles directly or through road side units (RSU) for sharing traffic information. The data dissemination in VANETs is a challenging issue as the vehicles have to share safety critical information in real time. The data distribution is usually done using broadcast method resulting in inefficient use of network resources. Therefore, to avoid the broadcast storm and efficiently use network resources, next forwarder vehicle (NFV) is selected to forward data to nearby vehicles. The NFV selection is based on certain parameters like direction, distance, and position of vehicles, which makes it a multicriteria decision problem. In this paper, analytical network process (ANP) is used as a multicriteria decision tool to select the optimal vehicle as NFV. The stability of alternatives (candidate vehicles for NFV selection) ranking is checked using sensitivity analysis for different scenarios. Mathematical formulation shows that ANP method is applicable for NFV selection in VANETs. Simulation results show that the proposed scheme outperforms other state‐of‐the‐art data dissemination schemes in terms of reachability, latency, collisions, and number of transmitted and duplicate data packets.

In recent years, an exponential increase has been witnessed in the population of the urban area worldwide, causing a significant increase in the use of transportation services. The traditional transportation system in metropolitan cities is overwhelmed, leading to many challenges. The Internet-of-Things- (IoT-) based technology has the potential to optimize transportation services in several ways. This paper highlights the challenges and consequences of an existing transportation system in Peshawar, Pakistan, in response to the rapid growth in population. Apart from the common issues, some areas in Peshawar are highly vulnerable to massive traffic jams. For this purpose, we have proposed an IoT-based framework for busy traffic junctions. The proposed framework considers the route selection problem as a game of two players, where Nash Equilibrium (NE) sets traffic for each route so that no individual can improve its performance by changing its strategy. NE calculates the traffic density from a roadside unit (RSU) collected data, which later detects and avoids traffic congestion by an alternate route selection. The framework in this paper provides a platform for the academia and transport department to convert the existing transportation system to IoT-based intelligent transportation in Peshawar, Pakistan.