Long range (LoRa) is one of the most widespread members of the low-power wide area network technologies family. Its ability to cover long distances combined with the robustness to radio frequency propagation effects make it an ideal solution in the deployment of low-cost sensor networks. However, two of the most critical issues that LoRa networks have to deal with are their scalability and reliability. The default medium access mechanism is the Pure-Aloha, which while ensuring low-energy consumption in end devices (EDs), due to its distributed and simple operation, does not offer any protection to packet collisions. As a result, the contention-based Pure-Aloha offers satisfactory operation in situations of low- and medium-offered load, while when the interconnected devices and the offered load increase, the number of collisions increases as well, resulting in a decrease of transmissions reliability. In this article we present a novel reinforcement learning (RL) assisted time division duplex protocol for LoRa networks. The operation of the proposed protocol is based on a combination of mechanisms and techniques that ensure the reliability required based on a predetermined value, combined with low-energy consumption for the EDs. The more important employed operating elements of the proposed protocol are the time division separation between uplink and downlink transmissions, the use of the channel activity detection mechanism, as well as the implementation of an RL mechanism to adjust basic parameters of the network operation. The effectiveness of the proposed protocol was evaluated through a number of simulation scenarios, where the results showed a significant improvement both in the reliability and fairness, as well as in the energy consumption of the EDs, compared to the existing and other relevant protocols from the literature.

The rapid growth of the Internet of Things (IoT) ecosystem has revolutionized the traditional wireless communication arena questioning the established networking solutions in terms of battery life, device and deployment cost, coverage, availability, and support for a massive number of devices. Motivated by this, a novel medium access control (MAC) protocol utilizing Long Range (LoRa) technology is presented, based on the classical time-division multiple access (TDMA) scheme, amended properly to accommodate the Wake-Up Radio (WuR) technology capabilities. Aiming for low-power wide-area network (LPWAN) applications, the proposed protocol improves data latency and offers augmented performance due to the absence of collisions. Various simulation scenarios were devised in order to evaluate the performance. The results instate the proposed algorithm as a promising access scheme for the IoT Realm.

During the last two decades, the IEEE 8201.11 family has multiplied data rate transmissions in wireless local area networks (WLANs). This progress is based on the adoption of new technologies concerning physical layer (PHY), combined with the enhancements in the medium access control (MAC) layer. Radio over Fiber (RoF) is a technology which can further extend this progress, combining the benefits of optical fiber and wireless networks. The current IEEE 802.11 MAC protocol is contention based, which leads to low utilization of the RoF PHY data rate and on the other hand cannot handle the effects of long propagation delays in RoF networks. In this paper, we present a new Hybrid Link Time Division Multiple Access (HL-TDMA) MAC protocol which addresses the above shortcomings and enhances the performance of both conventional and RoF WLANs. Then, through simulation, we evaluate the performance of the proposed protocol, which is shown to be superior to relevant RoF WLAN protocols in the literature.

Predicting the footfall in a new brick-and-mortar shop (and thus, its prosperity), is a problem of strategic importance in business. Few previous attempts have been made to address this problem in the context of big data analytics in smart cities. These works propose the use of social network check-ins as a proxy for business popularity, concentrating however only on singular business types. Adding to the existing literature, we mine a large dataset of high temporal granularity check-in data for two medium-sized cities in Southern and Northern Europe, with the aim to predict the evolution of check-ins of new businesses of any type, from the moment that they appear in a social network. We propose and analyze the performance of three algorithms for the dynamic identification of suitable neighbouring businesses, whose data can be used to predict the evolution of a new business. Our SmartGrid algorithm reaches a performance of being able to accurately predict the evolution of 86% of new businesses.

A Multi-Channel Cognitive MAC Protocol for adhoc cognitive networks that uses a distributed learning reinforcement scheme is proposed in this paper. The proposed protocol learns the Primary User (PU) traffic characteristics and then selects the best channel to transmit. The scheme, which addresses overlay cognitive networks,avoids collision with the PU nodes and manages to exceed the performance of the less adaptive statistical channel selection schemes in normal and especially bursty traffic environments. The simulation analysis results have shown that the performance of our proposed scheme outperforms that of the CREAM-MAC scheme.

Power management has been advanced on a crucial factor in the design of modern access networks. Furthermore, the proliferation of optical networking in the last mile led major Telecom unions, such as the International Telecommunication Union (ITU), to emerge energy consumption as a critical objective of the next generation passive optical networks (NG-PONs). In particular, the standardization of the 10-gigabit-capable PON (XG-PON) entails well-defined specifications towards power management and energy reduction, especially regarding the power control of optical terminal devices such as the optical network units (ONUs). In this way, the optical line terminator (OLT) along with ONUs are able to cooperate with each other in order to succeed energy reduction, by applying doze or cyclic sleep periods to idle ONUs. However, the sleep period determination remains a quite challenging research area. In this study, we endeavor to provide XG-PON networks with an effective forecasting mechanism that is capable of estimating the time duration of the forthcoming sleep session. To this end, we apply the exponential smoothing technique to best estimate the sleep duration based on the monitoring time series observations. The obtained evaluation results sound quite promising, since the proposed model accomplishes to advance the trade-off between the energy reduction and network efficiency.

Honeypots are systems aimed at deceiving threat agents. In most of the cases the latter are cyber attackers with financial motivations, and malicious software with the ability to launch automated attacks. Honeypots are usually deployed as either production systems or as research units to study the methods employed by attackers. In this paper we present the results of two distinct research honeypots. The first acted as a malware collector, a device usually deployed in order to capture self-propagating malware and monitor their activity. The second acted as a decoy server, dropping but logging every malicious connection attempt. Both of these systems have remained online for a lengthy period of time to study the aforementioned malicious activity. During this assessment it was shown that human attackers and malicious software are constantly attacking servers, trying to break into systems or spread across networks. It was also shown that the usage of honeypots for malware monitoring and attack logging can be very effective and provide valuable data. Lastly, we present an open source visualization tool which was developed to help security professionals and researchers during the analysis and conclusion drawing phases, for use with one of the systems fielded in our study.

The Web offers abundant amounts of information anytime, anyplace. Multimedia provision companies bloom and die, seeking to attract a viable share of the data dissemination market. More often than not, failure is attributed to unsuitable content orientation. The present work contributes a novel content specialization scheme for periodic, push-based data streaming services. Its goal is to maximize the ratio of user requests served within their respective deadlines. In this sense, optimality is proven to be achievable only by discarding all requests for certain data items, focusing solely on the streaming of the remaining ones. Thus, the service achieves the content specialization that yields the most clients. The methodology workflow is as follows. By means of mathematical analysis we quantify data compatibility in terms of boosting the system’s service ratio. A data selection algorithm is then proposed, which follows a sort-and-segment approach. Through simulations, the algorithm is shown to surpass related solutions, while coinciding with bruteforce results and analytical expectations.

In broadcast systems a central server collects client queries for a given set of data items and schedules the next broadcast accordingly. Since anonymity has been shown to promote user participation, the client queries do not contain a client ID. However, this choice leaves ample room for service misuse. This paper identifies a number of prospective attacks and quantifies their impact on the performance of the system. A novel query filtering mechanism named Sensor Swarm is introduced to tackle these attacks. Extensive simulations show that the proposed scheme can ensure smooth system operation even under heavy attacks. Thus, a broadcast system that employs the Sensor Swarm filtering scheme can sustain excessive client misbehavior before resolving to Turing tests or compromising user anonymity.

The present paper proposes a generalization of the square root rule for optimal periodic scheduling. The rule defines a ratio of item occurrences in a schedule, which minimizes the mean serving time. However, the actual number of each item's occurrences must be an integer. Therefore, the square root rule assumes large schedules, in order for the ratio to hold with acceptable precision. The present paper introduces an analysis-derived formula which connects the mean serving time and the size of the schedule. The relation shows that small schedules can also achieve near-optimal serving times. The analysis is validated through comparison with simulation and brute force-derived results. Finally, it is shown that minimizing the size of the schedule is also an efficient way of optimizing the aggregate scheduling cost.

Radio over fiber (RoF) technology is considered as an energy and cost-effective solution to cover the users' rapidly increasing demands for bandwidth and mobility. However, integration of a wireless and an optical network into a hybrid one needs the design of new protocols. In this work, a novel MAC protocol based on the MultiPoint Control Protocol (MPCP) is proposed. The network's decision center receives feedback from the mobile clients via MPCP's GATE/REPORT mechanism so as to efficiently allocate the bandwidth and the wavelength resources in a dynamic manner. The proposed MAC protocol adapts its operation according to the actual client traffic demands. Simulation results reveal the superior performance of the proposed protocol compared to other similar competing proposals reported in the literature.

Mobile Worldwide Interoperability for Microwave Access (WiMAX) constitutes an attractive trademark for supporting wireless access to Intranets and the Internet. Scheduling and mapping processes are of paramount importance since they dramatically affect the network performance. This work endeavors to provide a robust mapping scheme for the downlink sub-frame with respect to various Quality of Service (QoS) constraints. Each downlink request creates rectangular regions, called Horizons, and the mapping technique applies a persistent probing of the Horizons recorded. The evaluation experiments conducted indicate that the scheme designed beneficially affects the system performance.

Knowledge of the clients' preferences in communication systems enables adaptive, precise resource allocation in real-time. The present work introduces a novel scheme for estimating the clients' preferences in wireless data broadcast systems. The proposed scheme relies on the principle of maximum entropy, with statistical moments of the clients' overall waiting time serving as inputs, and the probability distribution of the broadcast data items being the output. Observations regarding periodic broadcast scheduling enabled the reduction of the convergence time well below that of related well-known approaches, in both noisy and noiseless environments. Simulation results showed that the technique can be used for accurate adaptation in minimal times, compared to the alternatives.

In environments with locality of client demands, the use of multiple directional antennas at the Broadcast Server has been shown to increase performance. In many cases however, such broadcasting systems fail to exploit the full potential of the multiple antennas as they do not take into account the geographical distribution of clients within the coverage area of the system. This letter proposes an adaptive smart antenna-based wireless push system where the beamwidth of each smart antenna is altered based on the current placement of clients within the system area. Coupled with a modification of the broadcast schedule, the proposed approach significantly increases the performance observed by the system clients.

A novel periodic scheduling scheme for wireless environments is presented. The proposed scheme enables quality of service (QoS) agreements per data item, contrary to the per-dataset approach of existing solutions. Constraints abiding by any of the existing models (impatience, utility, and waiting time) are allowed to exist concurrently. The impact of individual agreements on the system's performance as a whole is analytically studied and quantified. Comparison with related approaches rendered the proposed scheme optimally efficient and flexible enough to serve as a basis for the implementation of specialized QoS metrics as well.

Wireless data broadcasting systems are typically assigned limited operational bandwidth due to their usually wide spatial coverage. This limitation raises issues of increased client waiting times, especially as the total size of the data to be disseminated increases. This problem could be partially or fully resolved by offloading part of the data to local mobile ad hoc subsystems. To this end, a novel merging of wireless data broadcasting and information hovering is proposed. The information hovering subsystem handles the least popular data items, allowing the most popular ones to be efficiently disseminated through the central wireless broadcasting. Both bandwidth-greedy and bandwidth-conserving cooperation algorithms are proposed. The validity of the proposed merged scheme is evaluated through simulation in a realistic MANET with locality of data demand. The results show considerably decreased mean waiting times in any case.

The current proliferation of mobile devices has resulted in a large diversity of hardware specifications, each designed for different services and applications (e.g. cell phones, smart phones, PDAs). At the same time, e-mail message delivery has become a vital part of everyday communications. This article provides a cost-aware study of an RSA-based e-mail protocol executed upon the widely used Apple iPhone 1,2 with ARM1176JZF-S, operating in an High Speed Downlink Packet Access (HSDPA) mobile environment. The proposed study employs formal analysis techniques, such as probabilistic model checking, and proceeds to a quantitative analysis of the email protocol, taking into account computational parameters derived by the devices' specifications. The value of this study is to form a computer-aided framework which balances the tradeoff between gaining in security, using high-length RSA keys, and conserving CPU resources, due to hardware limitations of mobile devices. To the best of our knowledge, this is the first time that probabilistic model checking is utilized towards verifying a secure e-mail protocol under hardware constrains. In fact, the proposed analysis can be widely exploited by protocol designers in order to verify their products in conjunction with specific mobile devices.

IEEE 802.16e allows for flexibly defining the relation of the downlink and uplink sub-frames' width from 3:1 to 1:1, respectively. However, the determination of the most suitable ratio is left open to the network designers and the research community. Existing scheduling and mapping schemes are inflexibly designed. In this paper, a novel adaptive mapping scheme is proposed aiming to dynamically adjust the downlink-to-uplink ratio, following adequately the modification of the load requests with respect to both downlink and uplink directions. A learning automaton is exploited in order to sense the performance of the downlink and uplink mapping processes and to determine the most appropriate length ratio of both sub-frames in order to maximize the network performance. The suggested ratio determination scheme is evaluated through realistic scenarios and it is compared with static schemes that maintain a fixed ratio. The results show that our proposed scheme introduces considerable improvement, increasing the network's service ratio and reducing the bandwidth waste.

Despite being a fundamental networking primitive, data broadcasting has so far received little attention in underwater networks. This paper proposes an adaptive push system for data broadcasting in underwater acoustic wireless networks with locality of client demands. It achieves adaptation of its broadcast schedule according to the a-priori unknown needs of the clients, it efficiently combats the problem of high latency of the underwater acoustic wireless environment and it exploits the bandwidth distance relationship of this environment in order to improve performance in topologies with locality of client demands. Simulation results show superior performance of the proposed approach compared to existing systems.

Push networks provide an efficient way of disseminating information from a central entity. The problem of broadcasting unpopular data items in a push-based wireless network downgrades performance, even when locality of demand is taken into consideration. This research confronts this problem by suggesting a method of discarding the broadcast of unpopular data items for predefined time intervals. Simulation results show that increase in performance is feasible according to the underlying network configuration.

A main design issue in a wireless data broadcasting system is to choose between push-based and pull-based logic: The former is used as a low-cost solution, while the latter is preferred when performance is of utmost importance. Therefore, the most significant advantage of a push system is the minimal cost. This fact implies that hardware limitations do exist in the case of push systems. As a consequence, every related proposed algorithm should primarily be cost-effective. This attribute, however, has been overlooked in related research. In this paper, popular broadcast scheduling approaches are tested from an implementation cost aspect, and the results render them only conditionally realizable. Moreover, a new, cost-effective, adaptivity oriented schedule constructor is proposed as a realistic, minimal-cost solution.

IEEE 802.16 (WiMAX) constitutes one of the most promising broadband access technologies for high-capacity and high-distance wireless access networks, supporting user mobility. The contribution of this paper is twofold. Firstly, a novel mapping scheme for IEEE 802.16 Mobile standard is introduced, applying horizon mapping. Secondly, an efficient adaptive prediction-based scheme is devised, which is able to adjust the downlink sub-frame capacity, accordingly to the traffic load, since the standard allows the downlink-to-uplink subframe ratio to be changeable from 3:1 to 1:1. The novel adaptive horizon burst mapping (AHBM) scheme is evaluated by simulation experiments, which indicate that the proposed scheme operates effectively and efficiently, by reducing the number of unserved users, the number of unserved traffic requests, and the portion of wasted bandwidth.

This paper proposes an adaptive push system for dissemination of data in underwater acoustic wireless networks. Apart from achieving adaptation of its broadcast schedule according to the a-priori unknown needs of the clients, the proposed system also efficiently combats the problem of high latency of the underwater acoustic wireless environment. Simulation results show superior performance of the proposed system in the underwater environment compared to adaptations of existing terrestrial push systems. Moreover, its performance is not affected by increasing the coverage area of the broadcast server.

This paper proposes an adaptive push system that operates efficiently in the high-latency environment of underwater acoustic wireless networks. Using a simple technique, the proposed system efficiently utilizes the scarce bandwidth resource of the underwater acoustic environment. Simulation results show superior performance of the proposed system compared to adaptations of existing terrestrial push systems. Moreover, they show that the efficient utilization of bandwidth, its performance can be improved up to 17%.

The six papers in this special issue represent both the theoretical and application flavors of adaptive and learning systems.

Quality of Service (QoS) support has become a key factor in designing Media Access Control (MAC) protocols. This paper introduces a novel scheduling scheme which supports priority based QoS in Wavelength Division Multiplexing (WDM) star networks. The proposed Interval-based Prioritized Orderly Scheduling Strategy (IPOSS) employs a collision-free scheduling approach and handles variable-length data packets. In practice, it is designed to handle real-time traffic, on the basis that each node may generate high- and low-priority packets with high-priority packets being scheduled prior to low-priority ones. Moreover, the proposed scheme differentiates the packets' schedule order, by prioritizing the long-length over the short-length packets. The performance of IPOSS is evaluated under Bernoulli traffic and simulation results indicate that the novel scheme achieves a significantly high throughput-delay performance for real-time traffic, without sacrificing the performance for non-real-time traffic.

The Broadcast Disks method is commonly used to schedule the data transmission in wireless push based networks. The performance of a system that utilizes this method depends upon several parameters. This paper presents the Optimization- Based Procedure (OBP), an analytical approach for estimating the optimal values of these parameters. The validity of the analysis is verified through simulation and comparison with other popular and traditional approaches. The new approach was found to be accurate and dominant in the vast majority of a wide set of test cases. It also provided a thorough insight of the system's stability and behavior in general.

Optical burst switching (OBS) has emerged as a viable switching alternative in backbone optical networks since it can support high data rates with an intermediate granularity compared to wavelength routing and optical packet switching. At the edges of an OBS cloud, packets are assembled to form bursts which enter the network core and are switched on the fly using bandwidth previously reserved by their control packets at each node. A key problem in OBS networks is the assignment of wavelengths to incoming bursts, i.e. the scheduling of bursts. This paper proposes two new techniques which are shown to improve burst scheduling algorithms by lowering their complexity. The first proposed technique is based on a triangular estimator that defines a ldquodrop zonerdquo; bursts that fall into this area are considered to have a very low probability of finding a suitable wavelength and as such, no effort is made to schedule them. According to the second approach, the drop zone is defined dynamically based on the burst drop history. Simulation results show that both approaches yield burst drop rates marginally higher or identical to the LAUC-VF scheduling algorithm while reducing the number of channel or void checks and thus the algorithm complexity and execution time.

Several well-known scheduling schemes like EATS and MSL seem to suffer notable performance degradation under the realistic conditions of non-uniform destinations and non-negligible transceiver tuning latencies that apply in practical WDM star networks. Our EMPS protocol has been shown to noticeably increase the performance of practical networks of this kind. Nonetheless, EMPS tries to balance the offered load by only considering the heads of the message queues maintained by the source nodes; therefore, its performance is degraded, especially for medium to high offered loads and for rather slowly tunable transceivers. In this article we propose the Minimization of Probability and Size of scheduling Latency (MinPSL) protocol, which examines each source node's message queue at a certain depth and reorders messages intelligently on the basis of receiver availability. MinPSL perfectly minimizes the probability of having to schedule two or more messages intended for the same destination simultaneously or at close times. The proposed scheme also minimizes the actual performance penalty incurred, when scheduling of consecutive messages with the same destination cannot be avoided. Extensive simulations are carried out in order to study the performance of MinPSL and compare it to EMPS under various conditions. The simulation results ascertain the fact that MinPSL always brings about a considerable performance improvement.

With the increasing popularity of wireless networks and mobile computing, data broadcasting has emerged as an efficient way of delivering data to mobile clients having a high degree of commonality in their demand patterns. This paper proposes an adaptive push system that operates efficiently in environments characterized by high broadcasting speeds and a-priori unknown client demands for data items. The proposed system adapts to the demand pattern of the client population in order to reflect the overall popularity of each data item. We propose a method for feedback collection by the server so that the client population can enjoy a performance increase in proportion to the broadcasting speed used by the server. Simulation results are presented which reveal satisfactory performance in environments with a-priori unknown client demands and under various high broadcasting speeds.

Web users' clustering is a crucial task for mining information related to users' needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to discover similarities in users' accessing behavior with respect to the time locality of their navigational acts. In this context, we present two time-aware clustering approaches for tuning and binding the page and time visiting criteria. The two tracks of the proposed algorithms define clusters with users that show similar visiting behavior at the same time period, by varying the priority given to page or time visiting. The proposed algorithms are evaluated using both synthetic and real data sets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time-aware user clustering approaches. These clusters contain users exhibiting similar access behavior in terms not only of their page preferences but also of their access time.

With the increasing popularity of wireless networks and mobile computing, data broadcasting has emerged as an efficient way of delivering data to mobile clients having a high degree of commonality in their demand patterns. This paper proposes a push system that continuously adapts to the demand pattern of the client population in order to reflect the overall popularity of each data item. The adaptation is accomplished using a simple feedback from the clients. We propose that the simple feedback is sent only from clients whose distance from the server does not incur a significant timing overhead for the acknowledgment of an item. Simulation results are presented which reveal satisfactory performance in highspeed environments with a-priori unknown and dynamic client demands.

Today’s wireless LANs require efficient integration of multimedia and traditional data traffic. Multimedia network applications are time-bounded and have stricter QoS demands. The IEEE 802.11e workgroup is standardizing a new QoS enhanced access scheme for wireless networks. It is based on a mechanism called Enhanced Distributed Channel Access (EDCA). EDCA seems capable of differentiating the traffic, however, it exhibits great overhead that limits the actually available bandwidth and degrades the overall performance. This work proposes an alternative protocol which could be used in place of EDCA. The Priority Oriented Adaptive Polling (POAP) is collision free, it prioritizes the different kinds of traffic, and it is able to provide QoS for all types of multimedia network applications, while efficiently supporting background data traffic. POAP compared to EDCA, provides higher channel utilization, distributes network resources to the mobile stations adapting to their real needs, and generally exhibits superior performance.

We present a new scheduling algorithm, which is a synthesis of all the remarkable features of pretransmission coordination-based protocols for broadcast and select star networks. The new algorithm follows a novel policy for switching, in order to improve the efficiency of the network. It is based on all notable elements of a specific family of protocols and tries to accommodate the different ways of scheduling. The protocol manages to bring some improvement, in terms of channel utilization and network throughput, which is proven by simulation results.

In many data broadcasting applications clients are grouped into several groups, each one located at a different region, with the members of each group having similar demands. This paper proposes a mechanism that exploits locality of demand in order to increase the performance of wireless data dissemination systems. It trades the received energy per bit redundancy at distances smaller than the radius of the service area for an increased bit rate and thus transmission speed for items demanded by clients at such distances. The bit rate for an item transmission is dynamically determined from the distance between the server's antenna to the group of clients that demand this item via a simple feedback from the clients. Additionally, a simple mechanism is introduced that protects performance around the geographical area of interest from degradation caused by clients that are located elsewhere and demand the same information items with clients inside that area

QoS/HyTDMA-HTS is a new collision-free MAC sublayer protocol which provides the well known HyTDMA-HTS protocol with QoS extensions which enable it to offer quality services in demanding environments with critical applications for which time sensitivity is a key issue. QoS/HyTDMA-HTS can operate in bursty traffic networks, successfully regulating QoS parameters of hosts running real-time services even under high traffic loads, while at the same time, the traffic shaping techniques which it employs, prevent other hosts from sending large amounts of traffic and monopolizing the available bandwidth.

We report an innovative solely optical architecture to implement the centralized wavelength conversion module of the CWC (controlled wavelength conversion) protocol [Papadimitriou, G.I. et al., 1999]. The proposed module was designed to distribute the conversion wavelengths to the corresponding converters in an unbiased manner taking into consideration the information carried by the control wavelength. The design of the optically controlled module is based on the use of optical logic circuits. These optical circuits distribute the available wavelengths by eliminating the need of optical to electronic translation of the network's feedback information and thus reducing drastically processing time. Furthermore, due to the all-optical nature of the network hub the reliability of the system is improved.

With the increasing popularity of wireless networks and mobile computing, data broadcasting has emerged as an efficient way of delivering data to mobile clients having a high degree of commonality in their demand patterns. This paper proposes a push system that continuously adapts to the demand pattern of the client population in order to reflect the overall popularity of each data item. The adaptation is accomplished using a simple feedback from the clients. We propose that the simple feedback from the clients can be sent at regular time intervals in order to avoid the overhead that would be an incurred for acknowledging all item broadcasts. Simulation results are presented which reveal satisfactory performance in environments where client demands change over time with the nature of these changes being unknown to the broadcast server

A new medium access control protocol for broadcast LANs, which is capable of achieving a high performance under bursty traffic conditions, is introduced. According to the proposed protocol, the network stations are separated into groups. All the groups are granted permission to transmit in a round-robin fashion. The main objective of the grouping algorithm is to have exactly one ready station in each group. In this way, idle slots and collisions are minimized and a nearly optimal throughput-delay performance is achieved. The grouping of stations is dynamically modified at each time slot according to the network feedback information. Due to the dynamic nature of the grouping algorithm, the protocol is capable of being adapted to the sharp changes of the stations' traffic.

An adaptive MAC protocol for distributed wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of a neural-based algorithm. The neural-based algorithm takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol is compared via simulation to TDMA and IEEE 802.11 and is shown to exhibit superior performance under bursty traffic conditions even when the network feedback is noisy.

A carrier-sense-assisted learning automata-based MAC protocol for wireless LANs, capable of operating efficiently under bursty traffic and unreliable channel feedback, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of learning automata. At each station, the learning automaton takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol utilizes carrier sensing in order to reduce the collisions that are caused by different decisions at the various mobile stations due to the unreliable channel feedback.

A new class of P-model absorbing learning automata is introduced. The proposed automata are based on the use of a stochastic estimator in order to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed stochastic estimator scheme, the estimates of the reward probabilities of actions are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic ones is more relaxed for actions that have been selected only a few times. In this way, actions that have been selected only a few times, have the opportunity to be estimated as "optimal," to increase their choice probability and consequently, to be selected. In this way, the estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be epsilon-optimal in every stationary random environment. Furthermore, extensive simulation results are presented that indicate that the proposed stochastic estimator scheme converges faster than the deterministic-estimator-based DP(RI) and DGPA schemes when operating in stationary P-model random environments.

In this letter, a new medium-access sublayer self-adaptive protocol for broadcast networks is introduced. This new protocol is capable of operating efficiently under heavily bursty traffic conditions and can handle stations, which transmit packet of arbitrary length. TDMA, RTDMA and other fixed-assignment protocols have various drawbacks, that learning protocols such as LTDMA are known to resolve. But as a result of LTDMS's fixed-sized timeslots, reduced channel usage, wasted timeslots, as well as network equipment overloading can occur. The VPL/LTDMA protocol, which is here introduced, is tested using real LAN traffic traces and is proved to be successfully resolving the above issues. In addition it achieves a high throughput-delay performance when operating under realistic traffic conditions.

A self-adaptive neural-based MAC protocol for distributed wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by means of a neural-based algorithm. The neural-based algorithm takes into account the network feedback information in order to update the choice probability of each mobile station. The proposed protocol is compared via simulation to TDMA and is shown to exhibit superior performance under bursty traffic conditions even when the network feedback is noisy.

A learning automata-based polling (LEAP) protocol for wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. We consider an infrastructure wireless LAN, where the access point (AP) is located at the center of a cell which comprises a number of mobile stations. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by the AP by means of a learning automaton. The learning automaton takes into account the network feedback information in order to update the choice probability of each mobile station. It is proved that the learning algorithm asymptotically tends to assign to each station a portion of the bandwidth proportional to the station's needs. LEAP is compared to the randomly addressed polling and group randomly addressed polling protocols and is shown to exhibit superior performance under bursty traffic.

An adaptive medium access control protocol for broadcast networks, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which grants permission to transmit at each time slot is selected by taking into account the network feedback information. In this way, the number of idle slots is minimized and the network performance is significantly improved. Furthermore, the portion of the bandwidth assigned to each station is dynamically adapted to the station's needs.

A TDMA-based randomly addressed potting protocol (TRAP) is proposed. TRAP employs a variable-length TDMA-based contention stage with the length based on the number of active stations. Simulation results are presented that reveal the superiority of TRAP against the RAP protocol in cases of medium and high offered loads. Furthermore the implementation of TRAP is much simpler than that of CDMA-based versions of RAP, since no extra hardware is needed for the orthogonal reception of the random addresses.

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learning automata have made a significant impact on all areas of engineering. They can be applied to a broad range of modeling and control problems, which are characterized by nonlinearity and a high degree of uncertainty. Learning automata have some key features, which make them applicable to a broad range of applications: they combine rapid and accurate convergence with a low computational complexity. Learning is defined as any permanent change in behavior as a result of past experience, and a learning system should therefore have the ability to improve its behavior with time, toward a final goal. In a purely mathematical context, the goal of a learning system is the optimization of a function not known explicitly [2]. Thirty years ago, Tsypkin [3] introduced a method to reduce the problem to the determination of an optimal set of parameters and then applied stochastic hill-climbing techniques. Tsetlin [4] started the work on learning automata during the same period. An alternative approach to applying stochastic hillclimbing techniques, introduced by Narendra and Viswanathan [5], is to regard the problem as one of finding an optimal action out of a set of allowable actions and to achieve this using stochastic automata. The difference between the two approaches is that the former updates the parameter space at each iteration while the latter updates the probability space. The stochastic automaton attempts a solution of the problem without any information on the optimal action. One action is selected at random, the response from the environment is observed, action probabilities are updated based on that response,

Although new high-bandwidth network technologies are being introduced and widely deployed, asynchronous transfer mode (ATM) is still considered one of the most important network technologies currently in use. A number of ATM switch architectures have been proposed in the literature. However, industry has shown that is better to use the well-known shared-medium technique in the design of these ATM switches. In this paper, four variations of a new distributed scheme are proposed for the arbitration of a shared bus of an ATM switch. These schemes are based on learning automata. By taking advantage of the bursty nature of ATM traffic, the new arbitration scheme demonstrates superb performance compared to the time division multiple access (TDMA) scheme.

Due to its fixed assignment nature, the well-known time division multiple access (TDMA) protocol suffers from poor performance when the offered traffic is bursty. In this paper, an adaptive TDMA protocol, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which is granted permission to transmit at each time slot is selected by means of learning automata (LA). The choice probability of the selected station is updated by taking into account the network feedback information. The system which consists of the LA and the network is analyzed and it is proven that the choice probability of each station asymptotically tends to be proportional to the probability that this station is not idle. Although there is no centralized control of the stations and the traffic characteristics are unknown and time-variable, each station tends to take a fraction of the bandwidth proportional to its needs. Furthermore, extensive simulation results are presented, which indicate that the proposed protocol achieves a significantly higher performance than other well-known TDMA protocols when operating under bursty traffic conditions.

An absorbing learning automaton which is based on the use of a stochastic estimator is introduced. According to the proposed stochastic estimator scheme, the estimates of the reward probabilities are computed stochastically. Actions that have not been selected many times have the opportunity to be estimated as optimal, to increase their choice probabilities, and consequently, to be selected. In this way, the automaton's accuracy is significantly improved. This proposed automaton is proven to be absolutely expedient in all stationary environments, while the simulation results demonstrate that the proposed scheme achieves a significantly higher performance compared with deterministic estimator based schemes.

An adaptive protocol for WDM passive star networks, which is capable of operating efficiently under bursty, and correlated traffic, is introduced. According to the proposed protocol, the stations which are granted permission to transmit at each time slot, are selected by taking into account the network feedback information. Although the traffic parameters are unknown and time-variable, the bandwidth of each wavelength is allocated to the stations according to their needs. In this way, the number of idle slots is reduced, resulting in a significant increase of the network throughput.

Due to its fixed assignment nature, the well-known TDMA protocol suffers from poor performance when the offered traffic is bursty. In this paper, a new time division multiple access protocol which is capable of operating efficiently under bursty traffic conditions is introduced. According to the proposed protocol, the station which grants permission to transmit at each time slot is selected by means of stochastic estimator learning automata. The system which consists of the automata and the network is analyzed and it is proved that the probability of selecting an idle station asymptotically tends to be minimized. Therefore, the number of idle slots is drastically reduced and consequently, the network throughput is improved. Furthermore, due the use of a stochastic estimator, the automata are capable of being rapidly adapted to the sharp changes of the dynamic bursty traffic environment. Extensive simulation results are presented which indicate that the proposed protocol achieves a significantly higher performance than other well-known time division multiple access protocols when operating under bursty traffic conditions.

A client/server model which employs a polling policy as its access strategy is considered. We propose a learning-automata-based approach for polling in order to improve the throughput-delay performance of the system. Each client has an associated queue and the server performs selective polling such that the next client to be served is identified by a learning automaton. The learning automaton updates each client's choice probability according to the feedback information. Simulation results have shown that the proposed polling policy is beneficial in comparison to the conventional round-robin polling when operating under bursty traffic conditions.

A new protocol for WDM star networks is introduced. According to the proposed Centralized Packet Filtering (CPF) protocol, more than one stations share each wavelength and transmit their packets in a random access fashion. An array of electrooptic tunable filters, which is placed at the network hub, allows at most one packet per wavelength to pass to the star coupler; at each time slot. In this way, channel collisions are eliminated. Furthermore, the selection of the passing packets is implemented in such a way that receiver conflicts are avoided.

The centralized filtering of the transmitted packets has been the base of a family of high-performance protocols for WDM star networks. Protocols of this family have been proved to achieve a significantly higher performance than the well-known contention-oriented or round-robin protocols. However, the need for electronic control of the filtering mechanism as well as the use of slowly tunable acoustooptic filters have been limiting factors in their performance. Furthermore, since the network hub of WDM star networks represents a single point of failure, the extensive use of electronic circuits at this point reduces the reliability of the system. In this paper, an all-optical centralized protocol is introduced. According to this protocol, the passing of the transmitted packets to the star coupler is controlled by means of optical logic circuits, without the need of optical to electronic translation or electronic processing of the network feedback information. In this way, the processing time is drastically reduced, while the need for acoustooptic filters is eliminated. Therefore, a significant performance improvement is achieved. Furthermore, due to the all-optical nature of the network hub, the reliability of the system is improved. The performance of the proposed protocol is studied via extensive analytical and simulation results which indicate that a WDM star network operating under this protocol achieves a high throughput-delay performance under any load conditions.

The increasing bandwidth demands of the emerging new generation of computer communication networks have led to the utilization of optical fiber as a transmission medium. A new receiver conflict avoidance algorithm for wavelength-division multiplexing (WDM) broadcast-and-select star networks is introduced. The proposed algorithm is based on the use of learning automata in order to reduce the number of receiver conflicts and, consequently, improve the performance of the network. According to the proposed scheme, each node of the network is provided with a learning automaton; the learning automaton decides which of the packets waiting for transmission will be transmitted at the beginning of the next time slot. The asymptotic behavior of the system, which consists of the automata and the network, is analyzed and it is proved that the probability of choosing each packet asymptotically tends to be proportional to the probability that no receiver conflict will appear at the destination node of this packet. Furthermore, extensive simulation results are presented, which indicate that significant performance improvement is achieved when the proposed algorithm is applied on the basic DT-WDMA protocol.

A new class of learning automata is introduced. The new automata use a stochastic estimator and are able to operate in nonstationary environments with high accuracy and a high adaptation rate. According to the stochastic estimator scheme, the estimates of the mean rewards of actions are computed stochastically. So, they are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic estimator's contents is more relaxed when the latter are old and probably invalid. In this way, actions that have not been selected recently have the opportunity to be estimated as "optimal", to increase their choice probability, and, consequently, to be selected. Thus, the estimator is always recently updated and consequently is able to be adapted to environmental changes. The performance of the Stochastic Estimator Learning Automaton (SELA) is superior to the previous well-known S-model ergodic schemes. Furthermore, it is proved that SELA is absolutely expedient in every stationary S-model random environment.>

A new absorbing multiaction learning automaton that is epsilon-optimal is introduced. It is a hierarchical discretized pursuit nonlinear learning automaton that uses a new algorithm for positioning the actions on the leaves of the hierarchical tree. The proposed automaton achieves the highest performance (speed of convergence, central processing unit (CPU) time, and accuracy) among all the absorbing learning automata reported in the literature up to now. Extensive simulation results indicate the superiority of the proposed scheme. Furthermore, it is proved that the proposed automaton is epsilon-optimal in every stationary stochastic environment.>

A receiver collision avoidance algorithm for WDM broadcast-and-select star networks is introduced. It is based on the use of learning automata to reduce the number of receiver collisions and, consequently, to improve the performance of the network. Each station has a learning automaton that decides which of the packets waiting for transmission will be transmitted at the beginning of the next time slot. The learning automaton used is a multifeedback automaton, specially designed for the receiver collision avoidance problem of WDM broadcast-and-select star networks. The asymptotic behavior of the system, which consists of the automata and the network, is analyzed. The probability of choosing each packet asymptotically tends to be proportional to the probability that no receiver collision will appear at the destination node of this packet. Extensive simulation results indicate that a significant performance improvement can be achieved when the algorithm is applied on the basic DT-WDMA protocol.>

A new approach to the design of S-model ergodic learning automata is introduced. The new scheme uses a stochastic estimator and is able to operate in nonstationary environments with high accuracy and high adaptation rate. The estimator is always recently updated and, consequently, is able to be adapted to environmental changes. The performance of the stochastic estimator learning automation (SELA) is superior to that of the previous well-known S-model ergodic schemes. Furthermore, it is proved that SELA is absolutely expedient in every stationary S-model random environment.>