In an Internet of Things (IoT) environment (e.g., smart home), several IoT devices may be available that are interconnected with each other. In such interconnected environments, a faulty or compromised IoT device could impact the operation of other IoT devices. In other words, anomalous behavior exhibited by an IoT device could propagate to other devices in an IoT environment. In this article, we argue that mitigating the propagation of the anomalous behavior exhibited by a device to other devices is equally important to detecting this behavior in the first place. In line with this observation, we present a framework, called IoT Anomaly Detector (IoT-AD), that can not only detect the anomalous behavior of IoT devices but also limit and recover from anomalous behavior that might have affected other devices. We implemented a prototype of IoT-AD, which we evaluated based on open-source IoT device data sets as well as through real-world deployment on a small-scale IoT testbed we have built. We have further evaluated IoT-AD in comparison to prior relevant approaches. Our evaluation results show that IoT-AD can identify anomalous behavior of IoT devices in less than 2.12 ms and with up to 98% of accuracy.

Cloud computing is now being utilized as a prospective alternative for catering storage service. Security issues of cloud storage are a potential deterrent in its widespread adoption. Privacy breach, malicious modification, and data loss are emerging cyber threats against cloud storage. Recently, a fog server based three-layer architecture has been presented for secure storage employing multiple clouds. The underlying techniques used are Hash-Solomon code and customized hash algorithm in order to attain the goal. However, it resulted in loss of smaller portion of data to cloud servers and failed to provide better modification detection and data recoverability. This paper proposes a novel fog-centric secure cloud storage scheme to protect data against unauthorized access, modification, and destruction. To prevent illegitimate access, the proposed scheme employs a new technique$Xor - Combination$to conceal data. Moreover,$Block - Management$outsources the outcomes of$Xor - Combination$to prevent malicious retrieval and to ensure better recoverability in case of data loss. Simultaneously, we propose a technique based on hash algorithm in order to facilitate modification detection with higher probability. We demonstrate robustness of the proposed scheme through security analysis. Experimental results validate performance supremacy of the proposed scheme compared to contemporary solutions in terms of data processing time.

Designing an efficient, reliable, and stable algorithm for underwater acoustic wireless sensor networks (UA‐WSNs) needs immense attention. It is due to their notable and distinctive challenges. To address the difficulties and challenges, the article introduces two algorithms: the multilayer sink (MuLSi) algorithm and its reliable version MuLSi‐Co using the cooperation technique. The first algorithm proposes a multilayered network structure instead of a solid single structure and sinks placement at the optimal position, which reduces multiple hops communication. Moreover, the best forwarder selection amongst the nodes based on nodes’ closeness to the sink is a good choice. As a result, it makes the network perform better. Unlike the traditional algorithms, the proposed scheme does not need location information about nodes. However, the MuLSi algorithm does not fulfill the requirement of reliable operation due to a single link. Therefore, the MuLSi‐Co algorithm utilizes nodes’collaborative behavior for reliable information. In cooperation, the receiver has multiple copies of the same data. Then, it combines these packets for the purpose of correct data reception. The data forwarding by the relay without any latency eliminates the synchronization problem. Moreover, the overhearing of the data gets rid of duplicate transmissions. The proposed schemes are superior in energy cost and reliable exchanging of data and have more alive and less dead nodes.

The Internet of Health Things (IoHT) is an extended version of the Internet of Things that is acting a starring role in data sharing remotely. These remote data sources consist of physiological processes, such as treatment progress, patient monitoring, and consultation. The main purpose of IoHT platform is to intervene independently from geographically remote areas by providing low‐cost preventive or active healthcare services. Several low‐power biomedical sensors with limited computing capabilities provide IoHT’s communication, integration, computation, and interoperability. However, IoHT transfers IoT data via IP‐centric Internet, which has implications for security and privacy. To address this issue, in this paper, we suggest using named data networking (NDN), a future Internet model that is well suited for mobile patients and caregivers. As the IoHT contains a lot of personal information about a user’s physical condition, which can be detrimental to users’ finances and health if leaked, therefore, data protection is important in the IoHT. Experts and scholars have researched this area, but the reconstruction of existing schemes could be further improved. Also, doing computing‐intensive tasks leads to slower response times, which further worsens the performance of IoHT. We are trying to resolve such an error, so a new NDN‐based certificateless signcryption scheme is proposed for IoHT using the security hardness of the hyperelliptic curve cryptosystem. Security analysis and comparisons with existing schemes show the viability of the designed scheme. The final results confirm that the designed scheme provides better security with minimal computational and communicational resources. Finally, we validate the security of the designed scheme against man‐in‐the‐middle attacks and replay attacks using the AVISPA tool.

xCalamities such as earthquakes and tsunami affect communication services by devastating the communication network and electrical infrastructure. Multihop relay networks can be deployed to restore the communication environment quickly in catastrophe‐stricken areas. However, performance in terms of throughput is affected by deploying the relay networks. In wireless local area networks (WLANs), the primary purpose of multiband transmission employing multihop relay networks is to increase the throughput and reduce the latency. In the future, wireless networks are believed to carry high throughput, more data rates, and less latency by expanding bandwidth‐demanding applications. Simultaneous multiband transmission in WLAN systems is considered to increase the coverage area without power escalation. Due to the inherent characteristics of different bands and channel conditions, transmission rates tend to be different. The impact of such conditions may cater to the disproportional distribution of data among bands, causing some of the bands to be overwhelmed, which incurs buffer overflow and packet loss. In contrast, the channel capacity of some of the bands remains underutilized. In this paper, we consider the channel conditions and transmission rates of each band on either side of the relay to address the problems mentioned above. Furthermore, this paper proposes a load distribution‐based end‐to‐end traffic scheduling technique to improve system performance. The simulation results demonstrate the effectiveness of our proposed method with maximizing throughput and minimizing end‐to‐end delay.

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

Internet of Things (IoT) is considered one of the world’s ruling technologies. Billions of IoT devices connected together through IoT forming smart cities. As the concept grows, it is very challenging to design an infrastructure that is capable of handling large number of devices and process data effectively in a smart city paradigm. This paper proposed a structure for smart cities. It is implemented using a lightweight easy to implement network design and a simpler data format for information exchange that is suitable for developing countries like Pakistan. Using MQTT as network protocol, different sensor nodes were deployed for collecting data from the environment. Environmental factors like temperature, moisture, humidity, and percentage of CO 2 and methane gas were recorded and transferred to sink node for information sharing over the IoT cloud using an MQTT broker that can be accessed any time using Mosquitto client. The experiment results provide the performance analysis of the proposed network at different QoS levels for the MQTT protocol for IoT‐based smart cities. JSON structure is used to formulate the communication data structure for the proposed system.

Periodic beacons in vehicular ad hoc networks are transmitted with high message frequency to achieve the higher level of awareness required for the vehicular safety applications. Currently, the existing 10-MHz control channel in the dedicated short range communication (DSRC) standard is not compliant with the communication requirements in vehicular safety applications, i.e., the stipulated amendments in the DSRC offer little relief to congestion caused by the periodic beacon transmissions on the control channel, which adversely affects the message reception. In this paper, we seek to address the problem of congestion with a transmit power adaptation approach, based on the principles of cooperative game theory. The proposed approach, Adaptive transmit power Cooperative Congestion Control (AC3), is designed to allow vehicles to select their transmit power autonomously with respect to their local channel congestion. Since the number of neighbours of each vehicle and their corresponding transmit power levels vary, AC3 requires that each vehicle reduces its transmit power fairly during congestion. Explicitly, the proposed approach introduces the notion of marginal contributions of vehicles towards congestion and determines a fair power decrease for vehicles using a shapely value system model. This model requires the vehicles with the highest marginal contributions to reduce the most transmit power and vice versa. The simulation results demonstrate the utility of the proposed approach (i.e., capability to determine fair power decrease for an effective congestion control).