The migration to wireless network from wired network has been a global trend in the past few decades. The mobility and scalability brought by wireless network made it possible in many applications. Among all the contemporary wireless networks, Mobile Ad hoc NETwork (MANET) is one of the most important and unique applications. On the contrary to traditional network architecture, MANET does not require a fixed network infrastructure; every single node works as both a transmitter and a receiver. Nodes communicate directly with each other when they are both within the same communication range. Otherwise, they rely on their neighbors to relay messages. The self-configuring ability of nodes in MANET made it popular among critical mission applications like military use or emergency recovery. However, the open medium and wide distribution of nodes make MANET vulnerable to malicious attackers. In this case, it is crucial to develop efficient intrusion-detection mechanisms to protect MANET from attacks. With the improvements of the technology and cut in hardware costs, we are witnessing a current trend of expanding MANETs into industrial applications. To adjust to such trend, we strongly believe that it is vital to address its potential security issues. In this paper, we propose and implement a new intrusion-detection system named Enhanced Adaptive ACKnowledgment (EAACK) specially designed for MANETs. Compared to contemporary approaches, EAACK demonstrates higher malicious-behavior-detection rates in certain circumstances while does not greatly affect the network performances.

Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%.

Localization awareness is a fundamental requirement in many Internet of Things (IoT) and other wireless sensor applications. The information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in this area is mostly geared towards multi-hop range-free localization algorithm as that only utilizes connectivity (neighbors) information. This work focuses on anchor-based, range-free localization algorithm, particularly in anisotropic networks. We observe that the pioneer Distance Vector Hop or DV-Hop algorithm, which provides accurate estimation in isotropic networks, can be enhanced to compute localization estimation for anisotropic networks with similar or comparable accuracy. The recently proposed algorithms for anisotropic networks are complex with communication and computational overheads. These algorithms may also be overkill for several location dependent protocols and applications. This paper proposes a scheme, called DV-maxHop, which reaches comparable accuracy quickly utilizing simpler, practical and proven variant of the DV-Hop algorithm. We evaluate the performance of our scheme using extensive simulation on several topologies under the effect of multiple anisotropic factors such as the existence of obstacles, sparse and non-uniform sensor distribution, and irregular radio propagation pattern. Even for isotropic networks, our scheme out-performed recent algorithms with lower computational overheads as well as reduced energy or communication cost due to its faster convergence. We also introduce the formulation and simulation of Multi-objective Optimization to obtain the optimal solution.

Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and hybrid delivery networks. The combination of AI and IoT improves predictive analytics, dynamic routing, and fleet management, but scalability and regulatory issues are still major concerns. Hybrid frameworks that integrate drones or Unmanned Aerial Vehicles (UAVs), ground robots, and conventional vehicles reduce energy expenditure and increase delivery range, especially in urban contexts. Furthermore, sustainable logistics approaches, including electric vehicle fleets and shared delivery infrastructures, provide promise for minimizing environmental impact. However, economic viability, legal frameworks, and infrastructure readiness still influence the feasibility of large-scale adoption. This review offers a perspective on the changing patterns in LMD, calling for regulatory evolution, technological advancement, as well as interdisciplinary approaches toward cost-effective, durable, and environmentally friendly logistics systems.

Recent advances in wireless technology and computing have paved the way to the unprecedented rapid growth in de- mand and availability of mobile networking and services coupled with diverse system/network applications. Such advances triggered the emergence of future generation wireless networks and services to address the increasingly strin- gent requirements of quality-of-service (QoS) at various levels. The expected growth in wireless network activity and the number of wireless users will enable similar growth in bandwidth-crunching wireless applications to meet the QoS requirements. Mobility prediction of wireless users and units plays a major role in efficient planning and manage- ment of the bandwidth resources available in wireless networks. In return, this efficiency will allow better planning and improved overall QoS in terms of continuous service availability and efficient power management. In this paper, we propose extreme learning machines (ELMs), known for universal approximation, to model and predict mobility of arbitrary nodes in a mobile ad hoc network (MANET). MANETs use mobility prediction in location-aided routing and mobility aware topology control protocols. In these protocols, each mobile node is assumed to know its current mobility information (position, speed and movement direction angle). In this way, future node positions are predicted along with future distances between neighboring nodes. Unlike multilayer perceptrons (MLPs), ELMs capture better the existing interaction/correlation between the cartesian coordinates of the arbitrary nodes leading to more realistic and accurate mobility prediction based on several standard mobility models. Simulation results using standard mobility models illustrate how the proposed prediction method can lead to a significant improvement over conventional methods based on MLPs. Moreover, the proposed solution circumvents the prediction accuracy limitations in current algorithms when predicting future distances between neighboring nodes. The latter prediction is required by some applications like mobility aware topology control protocols