Machine type communication (MTC) is a fundamental technology to realize the concept of fully connected world in fifth generation (5G) Internet of Things (IoT). The massive roll out of the MTC devices is a serious challenge for cellular networks from operational and management perspective, including massive access and network congestion. Many proposals have been put forward by the research community to cater to the nuisance of massive MTC (mMTC) access in cellular networks. Recently, data aggregation has attracted a lot of research attention owing to its robust ability to resolve the above-mentioned challenges. In this paper, we review the recent development in data aggregation techniques, including their application scenarios, design, and limitations. Commencing with the application scenarios and current challenges in the MTC network, the classification of various proposed solutions for massive access along with the family of data aggregation techniques is discussed in detail. By doing so, it provides an insight about the future design trends that can propel the current research efforts to curb the mMTC access in a cellular network.

Intelligent transport system (ITS), owing to their potential to enhance road safety and improve traffic management, have attracted attention from automotive industries and academia in recent years. The underlying technology—i.e., vehicular ad-hoc networks (VANETs)—provide a means for vehicles to intelligently exchange messages regarding road and traffic conditions to enhance safety. The open nature of ITS as wireless communication technology leads to many security and privacy challenges. These challenges pertain to confidentiality, authentication, integrity, non-repudiation, location privacy, identity privacy, anonymity, certificate revocation, and certificate resolution. This article aims to propose a novel taxonomy of security and privacy issues and solutions in ITS. Furthermore, categorization of security and privacy schemes in ITS and their limitations are discussed with various parameters—scalability, privacy, computational cost, communication overhead, latency—and various types of security attacks has been analyzed. This article leverages new researchers for challenges and opportunities related to security and privacy in ITS.

The accommodation of massive machine type communication (mMTC) in cellular networks brings up serious technical challenges due to concurrent massive access of MTC devices. These challenges may further be aggravated by the presence of delay tolerant and intolerant services in an MTC network. This article proposes a cooperative data aggregation (CDA) scheme by employing fixed data aggregator (FDA) and multiple mobile data aggregators (MDAs) to cater MTC devices having variable quality of service (QoS) requirements. In this vein, a distributedMDA selection algorithm is also proposed to designate an appropriate user equipment as aggregator. The proposed CDA scheme effectively caters the massive access and provides ubiquitous availability of the aggregating devices in the MTC network. In addition, the limited channel resources impel an FDA to schedule resources besides data aggregation. Therefore, a resource allocation scheme is also proposed to dynamically allocate channels to the MTC devices subject to their QoS requirements. The proposed resource scheduling scheme ensures that transmission requests from delay intolerant MTC devices are contented on priority basis. The proposed CDA and dynamic resource scheduling schemes are analyzed and compared with the existing data aggregation and resource scheduling schemes, respectively. The numerical results corroborate that our proposed CDA scheme in conjunction with dynamic resource allocation improves the outage probability, energy efficiency and system capacity by 30%, 25% and 44%, respectively as compared to the single FDA scheme.

Effective resource allocation has always been one of the serious challenges in wireless communication. A considerable number of machine type communication (MTC) devices in 5G with variable quality of service (QoS) aggravates this challenge even further. Existing Resource allocation schemes in MTC are usually considering signal to noise ratio (SNR), which provides preference to MTC devices based on distance rather than their QoS requirements. This paper proposes a resource allocation scheme with dynamic priorities for MTC devices with multiple radio access technologies (RATs). The proposed resource allocation scheme has two main parts namely medium access and resource allocation. The medium access leverages the broadcast nature of wireless signal and MTC devices' wait time to assign priorities using capillary band in a secure and integral way. At resource allocation, SNR, total induced transmission delay, and transmission-awaiting MTC devices are used to assign resources in the cellular band. The rumination of two-staged dynamic priorities in the proposed scheduling scheme brings significant performance improvements in outage and success probabilities. Compared to SNR-based schemes, the proposed mechanism performs well by expressively improving the outage and success probability by 20% and 30%, respectively.

Millimeter-wave or 60 GHz communication is a promising technology that enables data rates in multigigabits. However, its tremendous propagation loss and signal blockage may severely affect the network throughput. In current data-centric device-to-device (D2D) communication networks, the devices with intended data communications usually lay in close proximity, unlike the case in voice-centric networks. So the network can be visualized as a naturally formed groups of devices. In this paper, we jointly consider resource scheduling and relay selection to improve network capacity in 60 GHz based D2D networks. Two types of transmission scenarios are considered in wireless personal area networks (WPANs), intra and intergroup. A distributed receiver based relay selection scheme is proposed for intragroup transmission, while a distance based relay selection scheme is proposed for intergroup transmission. The outage analysis of our proposed relay selection scheme is provided along with the numerical results. We then propose a concurrent transmission scheduling algorithm based on vertex coloring technique. The proposed scheduling algorithm employs time and space division in mmWave WPANs. Using vertex multicoloring, we allow transmitter-receiver (Tx-Rx) communication pairs to span over more colors, enabling better time slot utilization. We evaluate our scheduling algorithm in single-hop and multihop scenarios and discover that it outperforms other schemes by significantly improving network throughput.

Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of IoT entities, AI-based solutions, particularly the deep reinforcement learning (DRL) strategy, have been intensively studied in both the academic and industrial fields. However, there are still many open challenges, namely, the lengthening convergence phenomena of the agent, network dynamics, resource diversity, and mode selection, which need to be tackled. A mixed integer non-linear fractional programming (MINLFP) problem is formulated to maximize computing and radio resources while maintaining quality of service (QoS) for every user’s equipment. We adopt the advanced asynchronous advantage actor-critic (A3C) approach to take full advantage of distributed multi-agent-based solutions for achieving energy efficiency in MECNs. The proposed approach, which employs A3C for computing offloading and resource allocation, is shown through numerical results to significantly reduce energy consumption and improve energy efficiency. This method’s effectiveness is further shown by comparing it to other benchmarks.