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Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay. [ABSTRACT FROM AUTHOR]

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This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the rapid growth of ad-hoc wireless technology amidst the huge demands of high data-rates, the simultaneous optimization of overall networking throughput and utility-proportional resource allocation is of utmost significance. The design of infrastructure-less ad-hoc wireless network with highly dynamic operating conditions can be intelligently modeled by using the artificial intelligence algorithms and machine learning techniques. In this work, we employ the meta-heuristic computational method of genetic algorithm for multi-criterion optimization of sigmoidal utility maximization and throughput efficiency. The iterative implementation of the evolutionary genetic algorithm is effectively enforced by considering the comprehensive application of eight disparate combinations of individual creation, selection, mutation and crossover operations. Through the simulation experiments, the impact of execution of these feasible composite models on the derived two-dimensional Pareto-optimal frontier, the criteria satisfaction ratio and the average spread of individual solutions is extensively investigated. Furthermore, these diverse genetic operations for retrieving the optimal solution to the proposed problem formulation are compared in terms of the estimated precision and time complexity metrics. Finally, the efficacy of our optimization model is significantly compared with the previous works in terms of achievable throughput. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Vehicles (UAVs) or drones has grown rapidly in both civilian and military operations over the last few decades. Multi-UAV systems are preferred to single-UAV systems, as they are more efficient and cost-effective when completing missions collaboratively. However, like other ad-hoc networks, UAV networks face the challenge of effective communication. Existing research in mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs) does not fully address the unique characteristics of UAV networks, which can exhibit varying levels of dynamism, intermittent links, and fluid topology. Moreover, drones need to communicate with each other and ground stations in Flying Ad-hoc Networks. Therefore, routing protocols in such networks must select the most effective paths for communication while ensuring reliable and stable data transmission. This article examines several recently developed routing protocols in UAV communication, detailing their construction methods. As routing is quite challenging in UAVs, where communication performance depends on various factors, this research introduces performance metrics to analyze the efficiency of these protocols. Routing protocols in UAV networks must adapt to high mobility, dynamic topology, intermittent links, power constraints, and changing link quality. Since the lifespan of UAV nodes is limited, seamless handovers are crucial, and the energy efficiency of protocols at different layers should also be considered. Although the reviewed protocols address several aspects of designing routing protocols for UAV communication, some challenges remain. Thus, this article provides a comprehensive exploration of routing protocols in UAV communication, along with a discussion of some open research areas.

This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme.

Rapid advancements in communication technology have sparked intense interest in Wireless Networks research. A wireless ad hoc network is vulnerable to the existence of rogue nodes and to ad hoc routing attacks due to the continuously changing topology, open environment, and absence of centralized security architecture. The vulnerability of ad hoc networks is the target of a wide range of routing attacks. Due to inherent restrictions of its routing protocols, Ad hoc Networks are susceptible to many kinds of Denial-of-Service Attacks (DoS). It is possible to develop a viable method to stop the attack of starting or forwarding bogus Route Requests (RREQs), which consume network resources and cause a denial of service to real nodes, by using Ad hoc On Demand Vector (AODV) routing protocol as the underlying protocol. We provide a Trust Aware Routing Approach in this work. With this method, we compute the node trust weights depending on node behavior. The implementation is carried out in the NS2 environment, and the outcomes are shown to show the efficacy of our strategy. As performance indicators, we use the packet delivery ratio, network throughput, routing overhead, and quantity of packets received by the destination. [ABSTRACT FROM AUTHOR]

The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority. The intensity of the harm largely depends on the attacker’s intentions during hostile assaults. As a result, the loss of Information, power, or capacity may occur. The authors propose an Enhanced Trust-Based Secure Route Protocol (ETBSRP) using features extraction. First, the primary and secondary trust characteristics are retrieved and achieved routing using a calculation. The complete trust characteristic obtains by integrating all logical and physical trust from every node. To assure intermediate node trustworthiness, we designed an ETBSRP, and it calculates and certifies each mobile node's reputation and sends packets based on that trust. Connection, honesty, power, and capacity are the four trust characteristics used to calculate node reputation. We categorize Nodes as trustworthy or untrustworthy according to their reputation values. Fool nodes are detached from the routing pathway and cannot communicate. Then, we use the cryptographic functions to ensure more secure data transmission. Finally, we eliminate the untrustworthy nodes from the routing process, and the datagram from the origin are securely sent to the target, increasing throughput by 93.4% and minimizing de [ABSTRACT FROM AUTHOR]

Several nonterminal nodes in the ad-hoc sensor network architecture are involved in effectively communicating data. There are not enough nodes other than the terminals to process sensor data and send it between nodes. Because of this, the exchange of sensor data relies on devices capable of predicting events and responding quickly. Identifying the cluster head is essential to the network's long-term viability and operational efficiency. This paper proposes a robust probabilistic buckshot approach to identify the appropriate nodes, and the smooth handover mechanism in the corresponding cycles is mechanized. The proposed model also employs a heuristic algorithm named HARIS to identify the best cluster head by analyzing the residual energy associated with each sensor node over multiple iterations. The data exchanged among the nodes is encrypted using a lightweight accumulative data encryption model to ensure the confidentiality of the data. The proposed model is evaluated using various statistical analysis metrics like node availability, computational delay, throughput, and network lifetime. The proposed model outperforms the existing energy-sensitive sensor network models by 20–23%. [ABSTRACT FROM AUTHOR]

Ad-hoc networks have unique properties, making it impossible to execute the current existing network security policy successfully. Based on studying the Ad-hoc network, the Cloud Security theory will be applied to it. We apply Direct Blind Authentication theory and trusted computing platform hardware modules to optimize the Ad-hoc networks' authentication links. The program successfully addresses the security concerns of Ad-hoc nodes, strengthening the Ad-hoc network's defenses. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient method of reconstructing interconnections when the terminals of each plane change in real-time situations where randomly divided planes are interconnected. To connect all terminals when the terminals of each plane are changed, we usually reconstruct the interconnections between all terminals. This ensures a minimum connection length, but it takes considerable time to reconstruct the interconnection for the entire terminal. This paper proposes a solution to obtain an optimal tree close to the minimum spanning tree (MST) in a short time. The construction of interconnections has been used in various design-related areas, from networks to architecture. One of these areas is an ad hoc network that only consists of mobile hosts and communicates with each other without a fixed wired network. Each host of an ad hoc network may appear or disappear frequently. Therefore, the heuristic proposed in this paper may expect various cost savings through faster interconnection reconstruction using the given information in situations where the connection target is changing. [ABSTRACT FROM AUTHOR]

The implementation of the optimal Huffman coding technique for achieving proficient data compression, lower information redundancy and minimal utilization of the scarce bandwidth is essential to handle the effective processing of massive network data. In this paper, we employ cross-layer design technique among the data link, network and presentation layers of the traditional protocol stack to develop the improved DSDV routing protocol based on n-ary Huffman coding procedure. The entropy-based adaptive prefix codewords with variable length are assigned to the probability of packet successful delivery across the ad-hoc wireless network. Several coding and network efficiency parameters are deployed to assess the performance of the proposed routing scheme under three distinctive IPv4 network scenarios. These investigated network protocols include the default IPv4, multi IPv4 and dynamic IPv4 routing methodologies. Diversified simulation settings are employed with deviating network size to measure the multitude of essential wireless network characteristics incorporating the average delay, packet error rate, packet delivery fraction, data rate, ping loss rate, entropy rate, and reception cache hit. In addition, the set of key data compression/coding indicators are examined through comprehensive numerical analysis such as codeword length, source information rate, mean redundancy, and coding efficiency. Beyond, we significantly compare the performance of our developed cross-layer coded mobile routing model with several previous algorithms to validate its enhanced feasibility and superiority in terms of crucial network operation metrics such as throughput, packet drop rate, mean delay and packet delivery ratio. [ABSTRACT FROM AUTHOR]

Simple Epidemic Algorithm (SEA) is a social protocol used in the Pocket Switched Network (PSN) technology. SEA infects an entire population. We have defined a Secure Simple Epidemic Algorithm (SSEA) for PSN where a security condition controls the traffic. SSEA doesn't infect a global population. As the Internet Of Things (IOT) is with no specific definition, we have proposed a new model of IOT. In this latter, PSN that uses the developed SSEA guarantees the exchange of information. To best understand, we have defined a small model with four communities and an "EXTERNAL". Nodes traveling between communities have different security degrees. The security degree reflects the number of communities to which the node belongs and defines the security condition of SSEA. The exchange of information relies on the cooperative nodes. Supplying help and extra services in time and space identify the cooperation. In the best case, SSEA infects nodes with high security degrees and reduces the network communication cost. [ABSTRACT FROM AUTHOR]

Current efforts for providing an efficient dynamic source routing protocol (DSR) for use in multi-hop ad-hoc wireless are promising. This is since DSR has a unique characteristic in that it uses source routing, instead of relying on the routing table at each intermediate device. This study addresses the current challenges facing DSR protocol in terms of the dynamic changes of the route and how to update such changes into the route cache of the DSR. The challenges typically persist when a sudden route break occurs resulting in a delay in updating the new node location into the cache of the DSR protocol. For that, this study proposes a novel algorithm to improve the cache updating of DSR protocol in the ad-hoc network using dominating set-based routing (DBR). In DBR, the dominating nodes establish node update cache in accordance with the characteristics of the new route. Network Simulator version 2 (NS2) was used to implement and evaluate the proposed algorithm. A comparison of certain performance metrics was carried among the proposed DSR-DBR, DSR-route-cache, and DSR-original in the transmission control protocol and user datagram protocol. The DSR-DBR performance result showed a significant improvement in the average throughput, average end-to-end delay, average discovery time and routing overhead. [ABSTRACT FROM AUTHOR]

In hostile environment, security is a key problem for confined communication between mobile nodes. Adversaries can combine active and passive attacks against interceptable routing in routing messages and data packets in hostile environments. Mobile nodes are used in ad hoc networks to allow communication outside of the range of wireless transmission. Ad hoc network routing protocol attacks cause problems on network performance and dependability. Because ad-hoc has no obvious defenses, it may be accessed by both legitimate network users and malevolent attackers. Because data is transferred through intermediary routers, where several nodes can quit or link to network at any moment, problems arise when nodes are mobile and inadequate routing algorithms allow a user to update or modify information during data transmission. A black-hole is a serious security problem in ad-hoc networks that may be readily exploited by taking advantage of a weakness in on-demand routing protocols. This paper proposed weight based trust approach against black-hole attack. In this approach we compute weight of all network nodes on the basis of threshold computation. The implementation is done in the Network Simulation 2 and the results are presented. [ABSTRACT FROM AUTHOR]

MANET is standing for Network as Mobile Ad-hoc which is a self-directed mobile handlers group that communicates over relative bandwidth-constrained wireless channels. Many services with different classes of Quality of Services (QoS) could be provided through the MANET such as data, voice, and video streaming. Thus, efficient packets routing is an essential issue especially over this kind of burst channel. To settle this issue, many scheduling techniques are proposed to reduce the packets dropping and channel collision when a huge demand of data is transferred from a sender to a receiver. In this paper, four MANET scheduling algorithms are selected and investigated in mobile ad hoc networks which are Strict Preference (SP), Round Robin (RR), Weighted Round Robin (WRR), and Weighted Fair (WF). The network simulator EXata 2.0.1 is used to build the scenario which is consist of 50 nodes and performed the simulation. The results showed the performance metrics difference of the network such as the throughput and the end-end delay as well as queuing metrics like peak queue size, average queue length, in queue average time, and dropped of whole packets. Regrading throughput, the SP algorithm has a greater throughput than WF, RR, and WRR by 4.5%, 2.4%, and 1.42%, but WRR has outperformed others regarding the end-end delay. Moreover, WRR represents the best scheduling algorithm regarding both peak queue size since its greater than RP, WF, and WRR by 10.13%, 9.6%, and 5.32%, in order, and average output queue length, in contrast, WRR worsts more time in queuing but it is the best in preventing the packets from dropping. [ABSTRACT FROM AUTHOR]

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep $Q$ -learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ($ETX$) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a joint transceiver beamforming design method for hybrid full-duplex (FD) and half-duplex (HD) ad-hoc networks to cancel co-channel interference, thereby to improve system spectral efficiency. To characterize network performances, we derive a general expression of transmission capacity upper bound (TC-UB) plus its two compact versions by using a stochastic geometry model. Due to the proposed beamforming design and hybrid-duplex consideration, the exact TC and conventional methods to obtain TC-UBs are not applicable. This motivates us to exploit the UB of the largest eigenvalue of desired signals, Alzer’s inequality for the incomplete gamma function, and dominating interference region to formulate one general TC-UB and two of its compact versions. The numerical results show that the proposed beamforming method outperforms the existing beamforming strategies in terms of exact TC, especially when the number of transmit antennas is larger than the number of receiver antennas per node pair. In addition, the derived general TC-UB can provide relatively close TC performance as the exact ones, and its two compact versions can at least give order-wise TC performance. Moreover, we find the break-even points, where FD outperforms HD with different system configurations. [ABSTRACT FROM AUTHOR]

Mobile ad-hoc network (MANET) is an infrastructure-less network of mobile nodes, connected by wireless links. Although mobility is the key characteristic of MANETs, yet the frequent movement of nodes may lead to link failure. This frequent topology change due to evasive mobility is one of the primary reason of Quality of service (QoS) degradation in conventional MANETs. If link failure time of already established root is estimated in advance, data packet loss can be avoided by exploring alternate path well within time. Thus, accurate link failure time estimation is one of the key areas that have drawn the attention of researchers of the ad-hoc networking community. This paper outlines a least-square quadratic polynomial regression-based technique for estimating link failure time by using the signal strength of received data packets. The distance between the transmitting and receiving nodes is calculated from the received signal strength of data packets. The authors have generated an optimal error quadratic model between this node distance and data packet arrival time. After quadratic model formulation, the link failure time is estimated by finding the moment at which the maximum communication range will be achieved for considered transmitter–receiver pair. A detailed analysis has been done for observing the behaviour of link failure time estimation accuracy of the proposed technique with varying network parameters. The repercussion of mobility variation on estimated link failure time has also been analysed. The proposed technique has also been compared with an existing interpolation-based technique. Network Simulator 2.35 has been selected as the primary simulation tool along with BonnMotion and MOVE tool for various mobility scenarios generation.

In last few years, a tremendous increase has been observed in the usage of portable electronic devices including smart phones, tablets, laptops, and wearables. These devices are produced by different manufacturers and work on different platforms. People surrounded by these devices need to interact with them during the meeting, presentation, class room and lots of other collaborative activities to share and receive information across the devices. Recent research trends lead towards better utilization of these mobile devices by connecting them together, whereas the interaction among these devices is still device centric and is dependent on expensive fixed software and hardware infrastructure. However, ad-hoc settings, where fixed infrastructure services do not exist, or may suspend the interaction across these devices, require specialized collaborative space. This research presents an architectural framework, named Ad-hoc Collaborative Space (ACS) that provides an abstraction layer by hiding the complexities of ad-hoc environment thus resulting into reduced application development time by providing the easy to use API's. The experimental evaluation based on different operating parameters shows that the proposed framework efficiently manages service registration, service discovery, synchronization, and connectivity between different devices.

Nowadays, Unmanned Aerial Vehicles (UAVs) have become the preferred, and sometimes the only support tool when facing critical scenarios such as earthquakes, search and rescue missions, and border surveillance. In these scenarios, deploying a UAV swarm instead of a single UAV can provide additional benefits when, for example, cargo carrying requirements exceed the lifting power of a single UAV, or when the deployment of several UAVs simultaneously can accelerate the accomplishment of the mission, and broaden the covered area. To this aim, in this paper we present MUSCOP, a protocol that allows multiple UAVs to perfectly coordinate their flight when performing planned missions. Experimental results show that the proposed protocol is able to achieve a high degree of swarm cohesion independently of the swarm formation adopted, and even in the presence of very lossy channels, achieving minimal synchronization delays and very low position offsets with regard to the ideal case.

This paper aims to create infrastructure-free networking capabilities for people in need such as when inadequate infrastructure including mobile cell towers and power supplies are damaged or when all communication means are restricted. In general, this means finding a way to communicate without mobile cell towers, repeaters, or Wi-Fi hotspots on mobile devices. This paper focuses on introducing an ad-hoc network based on Bluetooth where users can communicate without barriers. The proposed network implementation uses Google’s API Nearby Connection which is one of Google Play Services that can provide high bandwidth, low latency, encrypted data transfers between nearby devices in a fully-offline P2P mode. Kotlin language was used to program an application called BlueEmergency installed on Android devices to broadcast data as bytes payloads in a form of text messages from one node to all nodes in proximity using Bluetooth only. The test and verification of the system are performed showed that successful communication is achieved with the latest versions of both the Android operating system and Bluetooth. Finally, the designed network can be extended to cover large geographical areas by multi-hopping the messages between devices to make the system more efficient to use in the time of need.

The current work proposes to conquer the issue looked by the ordinary spectrum aggregated CRAHN strategy. Co-operative routing and range spectrum aggregation are two promising strategies for cognitive radio ad-hoc networks (CRAHNS). Propose a range spectrum collection based helpful routing protocol, named as routing protocol. The basic objective of spectrum aggregation based agreeable routing protocol is to give higher imperatives profitability, upgrade throughput, and diminishes framework delay for CRAHNS. In this work Stackelberg gaming hypothesis will be actualized to accomplish high throughput and decreased delay, and this likewise improves the spectrum and energy effectiveness. Gaming method will be conducted between different channels. After doing the game, winner node among each channel is shortlisted. Now the transmission will be towards the Base station, yet again the gaming progression is done, and among the shortlisted nodes the node with high strength is being denoted and this is considered as the path with high transmitting energy and efficiency. By choosing this route, we can able to increase the effectiveness of the system. The throughput and end to end delay can be derived by using various diversity techniques. The different output power can be estimated. Over 97% efficiency achieved by using Stackelberg gaming theory. [ABSTRACT FROM AUTHOR]

Due to quick and inexpensive deployment, decentralized wireless ad-hoc networks have many potential application domains. However, stringent resource constraints, user mobility, limited channel capacity and high error rate of packets received at the wireless network interfaces make the design and optimization of these networks a challenging task. To solve these issues involving different layers of the protocol stack, we propose a multivariable Fuzzy Logic based Cross-Layer Optimization (FLCLO) Algorithm. In this algorithm, multiple parameters from various layers are given as inputs to a fuzzy logic controller. In fuzzy multi-attribute decision making framework, several distinctive parameters with inherent uncertainties in practical wireless communication scenarios are efficiently characterized by the fuzzy linguistic information. The fuzzy logic optimization technique developed here makes decisions by simultaneously considering multiple criteria that affect network performance in terms of packets per second, number of packets lost, throughput, mean SNIR and number of collisions. The performance of the proposed crosslayer fuzzy algorithm is evaluated by conducting simulations in OMNeT++ with dynamic fuzzy logic system embedded on it. The experimental results obtained from the simulation show that FLCLO used for selecting an efficient next-hop node performs better in terms of reducing the average MAC delay, energy spent on packet transmissions, and packet error rate. A comparative analysis of FLCLO with the media access algorithm used by the original IEEE 802.11 protocol shows that FLCLO is 52-68% more energy-efficient, has 45% lower MAC delay and reduces the packet error rate by 71%. In addition, it guarantees a lower collision rate and reduced packet loss ratio when compared with the conventional IEEE 802.11 ad-hoc routing model in the same set up. Finally, the simulation results demonstrate that our algorithm offers considerable performance enhancement in terms of mitigated packet loss ratio, mean delay, and energy consumption in contrast to the previous related works existing in literature. [ABSTRACT FROM AUTHOR]

Ad-hoc Social Networks are formed by groups of nodes, designating a similarity of interests. The network establishes a two-layer hierarchical structure that comprises communication within-group and joining with other groups. This paper presents survey and future directions in four areas of establishing ad-hoc social network using mobile ad-hoc social network (MANET) that includes architecture or implementation features, Profile Management of users, Similarity Metric, and Routing Protocols. The survey presents the need to provide social applications over MANET, optimizing profile matching algorithms of users, and context aware routing protocols. Future directions include multi-hop social network applications that can be useful for users even in airplane mode and notifying over MANET when a user of profile with similar interest is nearby. [ABSTRACT FROM AUTHOR]

Abstract In cognitive radio (CR) ad-hoc network, the characteristics of the frequency resources that vary with the time and geographical location need to be considered in order to efficiently use them. Environmental statistics, such as an available transmission opportunity and data rate for each channel, and the system requirements, specifically the desired data rate, can also change with the time and location. In multi-band operation, the primary time activity characteristics and the usable frequency bandwidth are different for each band. In this paper, we propose a Q-learning-based dynamic optimal band and channel selection by considering the surrounding wireless environments and system demands in order to maximize the available transmission time and capacity at the given time and geographic area. Through experiments, we can confirm that the system dynamically chooses a band and channel suitable for the required data rate and operates properly according to the desired system performance.

Cognitive radio (CR) is an adaptive radio technology that can automatically detect available channels in a wireless spectrum and change transmission parameters to improve radio operating behavior. Due to the dynamic nature of spectrum availability and wireless channel condition, it is very hard to maintain reliable network connectivity. Cluster-based CR ad-hoc networks (CRAHN) arrange CR nodes into groups to effectively maintain reliable autonomous networks. Clustering in CRAHN supports cooperative tasks such as spectrum sensing and channel managements and achieves network scalability and stability. In this paper, we proposed a Q-learning based cluster formation approach in CRAHN, in which Q-value is used to evaluate each node's channel quality. To form a distributed cluster network, channel quality, residual energy and neighbor node/network conditions are considered. By exchanging each node's status information in terms of channels and neighbors, each node knows neighboring topology and which node is the best candidate for cluster head (CH). Distributed CH selection, the optimum common active data channel decision, and gateway node selection procedures are presented in this paper. The proposed mechanism can extend the network lifetime, enhance the reachability not only between member nodes but also with other cluster networks, it can also provide stable and reliable service using the selected data channel and avoid possible interference between neighboring ad-hoc clusters.