I- Introduction

II- Background and Related Works

III- System Model

IV- Analysis of Interference at SU

V- Analysis of Interference-Aware Information Dynamics

VI- Performance Evaluation

VII- Conclusion

References

Abstract

To fully empower sensor networks with cognitive Internet of Things (IoT) technology, efficient medium access control protocols that enable the coexistence of cognitive sensor networks with current wireless infrastructure are as essential as the cognitive power in data fusion and processing due to shared wireless spectrum. Cognitive radio (CR) is introduced to increase spectrum efficiency and support such an endeavor, which thereby becomes a promising building block toward facilitating cognitive IoT. In this paper, primary users (PUs) refer to devices in existing wireless infrastructure, and secondary users (SUs) refer to cognitive sensors. For interference control between PUs and SUs, SUs adopt dynamic spectrum access and power adjustment to ensure sufficient operation of PUs, which inevitably leads to increasing latency and poses new challenges on the reliability of IoT communications. To guarantee operations of primary systems while simultaneously optimizing system performance in cognitive radio ad hoc networks (CRAHNs), this paper proposes interference-aware flooding schemes exploiting global timeout and vaccine recovery schemes to control the heavy buffer occupancy induced by packet replications. The information delivery dynamics of SUs under the proposed interference-aware recovery-assisted flooding schemes is analyzed via epidemic models and stochastic geometry from a macroscopic view of the entire system. The simulation results show that our model can efficiently capture the complicated data delivery dynamics in CRAHNs in terms of end-to-end transmission reliability and buffer occupancy. This paper sheds new light on analysis of recovery-assisted flooding schemes in CRAHNs and provides performance evaluation of cognitive IoT services built upon CRAHNs.

Introduction

Cognitive Internet of Things (IoT) technology features advanced machine intelligence toward improved data sensing and analysis [1], [2], which is particularly appealing to applications involving sensor networks. To fully incorporate cognitive IoT technology in sensor networks, intelligent and efficient medium access control protocols enabling the coexistence of sensor networks with current wireless infrastructure and simultaneously optimizing the performance of IoT applications are emergent challenges. Consequently, in addition to cognitive power in data sensing and computation, another crucial factor toward cognitive sensor networks is the cognition in spectrum access in order to fully deploy cognitive IoT. Over the past decade, cognitive radio (CR) has received dramatic attention as it holds tremendous promise for increasing the utilization of scarce radio spectrum shared by primary (licensed) systems (PSs) and secondary (unlicensed or conditionally licensed) systems (SSs), e.g., cognitive sensor networks. In particular, recent works on cognitive IoT technology have identified CR as a critical building block that supports cognitive sensor networks [3]–[7], where PSs refer to existing wireless infrastructure and SSs refer to cognitive radio networks. Using CR terminology, secondary users (SUs) sense surrounding environment and adapt their operations around those of the primary users (PUs) to opportunistically exploit available resources while limiting their interference to PUs. In interweave paradigm [8], SUs seek and exploit the temporary spectrum opportunities without causing any interference to PUs. To further improve the spectrum usage, SUs in underlay paradigm are allowed to concurrently transmit with PUs as long as sufficient operation of PUs is ensured. When IoT applications are built upon CR-enabled cognitive sensor networks, the information delivery dynamics are jointly affected by the activities of PUs and CR medium access protocols, which inevitably leads to increasing latency for communications among IoT devices. This paper aims to study the data delivery dynamics of flooding schemes in cognitive sensor networks, which specifies the effect of packet delivery control and dynamic spectrum access on buffer occupancy and endto-end reliability in data transmission, and therefore provides a novel analysis framework for evaluating the performance of information delivery in cognitive sensor networks.

Abstract

Spectrum sharing between cellular and ad-hoc networks is studied in this work. Weak signals and strong interferences at the cell-edge area usually cause severe performance degradation. To improve the cell-edge users’ performance quality while keeping high spectrum efficiency, in this paper, we propose a cooperative spectrum sharing scheme. In the proposed scheme, the ad-hoc users can actively employ cooperative diversity techniques to improve the cellular network downlink throughput. As a reward, a fraction of the cellular network spectrum is released to the ad-hoc network for its own data transmission. To determine the optimal spectrum allocation, we maximize the ad-hoc transmission capacity subject to the constraints on the outage probability of the ad-hoc network and on the throughput improvement ratio of the cellular network. Both the transmission capacity of the ad-hoc network and the average throughput of the cellular network are analyzed using the stochastic geometry theory. Numerical and simulation results are provided to validate our analytical results. They demonstrate that our proposed scheme can effectively facilitate ad-hoc transmissions while moderately improving the cellular network performance.

Introduction

Cognitive spectrum sharing was recently studied to accommodate growing demands for wireless broadband access, which can alleviate the problem of under-utilization of licensed spectrum. Spectrum sharing techniques can be generally classified into three categories: interweave, underlay, and overlay [1]. For the interweave spectrum sharing, the secondary system can opportunistically access spectrum holes. For the spectrum underlay, secondary users (SUs) transmit simultaneously with primary users (PUs) under the constraint that interference caused by the SUs on the PUs must be below a certain threshold. For the spectrum overlay, SUs actively help primary data transmission in exchange for a spectrum access in time domain [2], spatial domain [3], or frequency domain [4]. The locations of SUs are usually fixed or restricted into a small area without suffering interference from other concurrent transmissions. It is nontrivial to extend the cooperative spectrum sharing to the secondary ad-hoc networks, as the topology changes frequently and the interference suffers from uncertainties caused by both random user locations and channel fadings.

The self-organizing, decentralized and infrastructure-less features of MANETs provide a promising solution for several real-world applications [1]. The nodes in a MANET function as both router and host, and communicate with other nodes which are not in its transmission range through intermediate nodes by establishing a route and then transferring the packets. Though nodes are considered to be trustworthy, however, a few nodes might be malicious and launch attacks to breach privacy [2]. Privacy requirement for MANET applications is challenging when compared to other applications because packets are transmitted over the wireless channel in multiple hops, which makes interception of the packet by attackers an easy task. Also, route maintenance due to frequent route and node failures increases the duration of data transfer, which can be utilized by attackers to launch attacks (like sender and receiver identification) by intercepting more packets. Over the past decade, many privacy protecting systems have been proposed and analyzed, which are based on pseudonyms, masking information, privacy policies, etc. [3–5]. In particular, existing works on the analysis of privacy protection system based on policies have mainly focused on determining the redundancy, incompleteness, etc. [6], and have not concentrated on the suitability of these privacy policies. This paper analyzes the privacy protection system by analyzing the performance of privacy policies and also determine the suitability of privacy policies to preserve privacy in MANETs.

MANETs have different aspects like security, IP addressing, routing, multiple access, service for handling wireless network communications. Network security is one of the major concerns in deploying the MANET applications, and thus, it gains interest among the researchers. In this section, the most important analysis is carried out in designing the secure framework, which would prevent the network from the attackers. Generally, MANETs are vulnerable in the operational functionality [1]; Interlopers can compromise the network activities by attacking any part of the network layers and Physical MAC; particularly, it affects network layer because of its poor routing algorithms, low-level batteries of the nodes, limited computational capacity of the nodes and undefined physical network location and short time nature of services in the wireless network [2]. Moreover, criterion followed for the measures of information security such as cryptography and key management scheme, have not given entire protection to the network [3]. Therefore, IDPS (intrusion and prevention system) are largely utilized to enhance the MANET security. An IDS is a combination of software and physical components, which highlights the system activities in order to identify undesirable activities and issues (Fig. 1). For example, illicit and destructive activity, that captures the activities of security policy failure and also identifies dissatisfactory level in the security mechanism [4]. IDS are mainly used for securing the network organization systems. The volatile development of the network frameworks can make the data exchange between the networks and computer, which is very crucial issues in the developing networking technology [5]. Meanwhile, it is a very difficult task to identify the attacker in the network which becomes hazardous in the network security, so, it needs to establish the proper procedure or protocol for security [6]. Intrusion generally refers to system attackers, which is causing vulnerable to various services such as hacking the information on the applications layer, host-based attack leads to login unapproved data or sensitive data that turns to be malware, viruses, worms and Trojan horses [7]. These types of attackers try to affect the confidentiality, integrity and accessibility of resources. Intrusions can also deny the administration’s activities, system network by neglecting responses to without reveal about the information being stolen or lost. Intrusion detection systems used for identifying unapproved resource utilization of a system and also discovers attackers on a system framework or networks.

Recent advances in micro-electro-mechanical systems (MEMS) technology have enabled the development of low-cost, low-power, multi-functional and small-size sensors to be embedded in mobile devices, such as smartphones, wearable devices and in-vehicle sensors. Many mobile devices come with Internet connectivity and embedded sensors (e.g., accelerometers, gyroscope, microphone, video camera, GPS, and speed sensors), thereby turning themselves into well-functioned sensor boxes to probe personal activities and environmental phenomena in the vicinity. Consequently, a new sensing paradigm named Mobile Crowd Sensing (MCS) comes into being to harness the potential of the widespread mobile sensors and describe the dynamic patterns of the physical world across a wide variety of application domains. These sensing capable mobile devices, which consist of sensing, data processing, and communicating components, represent a significant improvement over traditional sensors networks. Most existing sensor networks still require overwhelming expenditure and professional personnel for both deployment and long-term maintenance, which are unaffordable for individuals and small companies. As a result, many areas of interest are out of sensing coverage and the collected data are insufficient for various application requirements. Worse still, the underutilization of current sensor networks and sensor-equipped mobile devices are wasteful of existing investment, as the sensor motes are static and they stay in hibernation mode most of time. Comparatively, MCS offers several advantages over the traditional sensor network infrastructures. First, MCS is built on the already-existing mobile devices with broad network access (e.g., cellular base stations and wifi access points), which are globally widespread and ready to be used. The persuasiveness of smartphones on an unprecedented scale reduces the deployment cost to almost zero, while the traditional static sensor networks involve overwhelming deployment costs that cannot be afforded by individuals and small enterprises. Furthermore, the movement of mobile device carriers implicates high spatio-temporal coverage and increases the possibility of capturing unexpected events as compared to static sensor networks. Last but not the least, the human involvement could provide additional intelligence such as persuasive user feedback on the sensor data, thus promoting the quality of service and improving the user experience of sensing applications.

Three-dimensional mobile ad hoc networks (3D MANETs) are a class of flexible and distributed peer-to-peer networks, where mobile nodes move within 3D space and can communicate with each other via wireless link without any pre-existing infrastructure. As 3D MANETs can be rapidly deployed and flexibly reconfigured, they are appealing many critical applications: various military units communication (i.e., aircrafts, troops, and fleets) for modern combat, underwater vehicles communication for ocean surveillance, and unmanned aerial vehicles communication for disaster monitoring. To support these applications with different delay requirements in 3D MANETs, understanding the packet delivery delay performance in such networks is of fundamental importance. The packet delivery delay performance for 2D MANETs has been extensively studied in the literature, in terms of its order sense scaling laws with network size or its closedform analytical models (see Section II for related work). However, all the above work is conducted on 2D MANETs only. Recently, some initial work has focused on the study of performance for 3D MANETs, such as throughput capacity [1] and delivery rate [2], which are defined as the maximum packet input rate that the network can stably support and the probability that a packet is successfully transmitted to its destination, respectively. It is notable that the packet delivery delay performance in 3D MANETs has not been investigated, which significantly hinders their applications. Different from the aforementioned work, this paper studies the packet delivery delay performance in 3D MANETs. This paper made a significant improvement on our previous work of [3]. In [3], we only studied the packet delivery delay. In this paper, we study the packet delivery delay as well as corresponding relative standard deviation.

The mobile ad hoc network (MANET) is an emerging communication technology [1]. Its flexible and easy deployment has attracted different application areas, such as military [2], disaster recovery [3] and vehicular communication [4]. In simple terms, the MANET is an autonomous wireless network without fixed infrastructure, and it consists of nodes which can move dynamically [5]. A link (or called a hop) between two nodes exists if both nodes are in the communication range of each other. Multi-hop communication is popularly applied in the MANET to increase the overall network capacity and performance [6]. A multi-hop path connecting two nodes can be established by taking other nodes as relay nodes. Network reliability refers to the probability that the function of a network can meet the use requirement, and it is an important index to weigh network performance [7]. Network reliability has been researched in many fields, such as communication [8], transportation [9] and electric power [10], to design new systems or improve existing systems. The connectivity reliability and the performance reliability are two branches of network reliability, and both of them are important for the reliability evaluation of networks [11]. The connectivity reliability mainly studies the topology structure of networks, and it is defined as the probability that a certain set of nodes in a network are connected [12]. The performance reliability is related to network performance, such as transmission delay for a communication network, and it is defined as the probability that the performance of a network can meet the use requirement [11].

مقاله سال 2016

Emerging Trends in Networks and Computer Communications International Conference

در مناطق روستایی در آفریقا، شرایط متفاوت جغرافیایی، شامل  مناطق پر از تپه یا مناطق هموار ، بوته ها ، درختان و  گیاهان می شوند. در بعضی  موارد، شرایط زیر ساختی و جاده بسیار ضعیف هستند، آن را برای ارائه ی ضرورت تعمیر و نگهداری و پشتیبانی شبکه های ارتباطی چالش برانگیز و هزینه بر می سازد. وقتی یک گره به صورت آفلاین می رود گره های باقی مانده بایستی برای برقراری دوباره ی لینکها با هر یک و حفظ اتصال  توانا باشند. پروتکل مسیریابی باید کوتاه ترین مسیر جایگزین را پیدا کند و این مسیر را برای ارائه ی داده ها استفاده کند.  زمان تعمیر و نگهداری می تواند  کند باشد و ممکن است چند روز  برای مراجعه به گره ی معیوب در یک منطقه ی روستایی را بگیرد. با توجه به این، شبکه باید قادر باشد برای مدت طولانی با گره(ها) معیوب عمل کندو بهترین کیفیت ممکن از خدمات را ارائه دهد. در چند سال گذشته، شبکه های بی سیم مش (MWNs)  افزایشی در پژوهش و استفاده  به دلیل  مشخصات جذاب آنها که شامل هزینه استقرار کم را جلب کرده اند، که شامل هزینه ی گسترش که می باشد، یک هزینه ی کم گزینه ای برای پوشش شبکه ی گسترده و سهولت نگهداری با توجه به خصوصیات  خودشناسی آنها می باشد. در MWNs  یک افزایش در مقیاس پذیری، توان عملیاتی شبکه به کاهش تمایل دارد .در این مقاله، ما یک تجزیه و تحلیل عملکردی را  برای سناریوهای گره شکست و برای شبکه های دور سنجی روستایی با استفاده از سه پروتکل مانند، OLSR(یک پروتکل فعال)، DSR(یک پروتکل واکنشی) و HWMP(یک پروتکل ترکیبی)،انجام داده ایم. تجزیه و تحلیل عملکردی از این پروتکلها ، با استفاده از سه سناریوی توپولوژی شبکه بک هال (backhaul) صورت گرفته است. نتایج مشابه با استفاده از OMNET++ و چارچوب INETMANET  به دست آمده اند. معیارهای عملکردی برای تجزیه و تحلیل استفاده شدند و گم کردن بسته(packet loss) و تاخیر end-to-end  را مطالعه می کند. همچنین اینها فاکتورهای مهم در نظر گرفته شده  برای ارائه کیفیت تضمین شده از خدمات هستند (QoS).