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GICS follows the outline of Algorithm 20, Section 65 Naturally, the chromosome encodes the processor allocation, which is an indirect representation (Section 652) Hence, the construction of the schedule corresponding to each chromosome, which is necessary for the evaluation of its tness, requires the application of a heuristic As already mentioned, this is performed by a list scheduling heuristic under the involvement contention model (see later discussion) Strictly speaking, this means that a two-phase heuristic is applied multiple times in GICS once for each new chromosome To reduce the running time of the evaluation, the node order is determined only once at the beginning of the algorithm, namely, according to their bottom levels Like most GAs, GICS starts with a random initial population, enhanced with a chromosome representing a sequential schedule (all nodes are allocated to a single processor) The pool is completed by one allocation extracted from a schedule produced with a list scheduling heuristic (Section 841) Most other components are fairly standard In fact, the algorithm can even be employed for scheduling under the classic or the contention model, using a modi ed evaluation (ie, scheduling heuristic) Phase 2 List Scheduling From Section 52 it is known that the second phase can be performed with a simple list scheduling lgorithm The processor choice of list scheduling is simply a lookup from the given mapping A established in the rst phase Even though list scheduling under the involvement contention model was already studied in Section 841, it must be revisited as there are three alternatives for the scheduling of the edges when the processor allocation is already given (Section 832) Scheduling of the Edges The rst alternative corresponds to direct scheduling, and the procedures for the scheduling of a node and the calculation of the DRT are presented in Algorithms 23 and 24 The scheduling of an edge on the source processor must be modi ed (line 16 in Algorithm 23), since it is no longer necessary to assume the worst case Lines 1 5 (removing local edges) and line 9 (correcting the nish time) can be dropped completely, since the provisional scheduling of the edges is not necessary with the given processor allocation The other two alternatives are only distinguished from this procedure through the place where the edges are scheduled on the links and the destination processor So, for the second alternative, where the edge is scheduled on the links as a leaving edge, lines 8 and 10 of Algorithm 23 move to the for loop on lines 15 17 In the third alternative, the edge is also scheduled on the destination processor within this loop, and the for loop for the entering edges (lines 6 13) is completely dropped Of course, in the determination of the DRT the entering edges do not need to be tentatively scheduled, as the processor allocation is already given Complexity The complexity of list scheduling with a given processor allocation decreases slightly in comparison to list scheduling with start/ nish time minimization It is not necessary to tentatively schedule a node and its edges on every processor Thus,.

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A technique similar to SeRLoc is used in iRLoc [24], which has a greater accuracy but an increased computational and communication complexity Techniques like these can be used to protect the Localization Algorithm Component Another simple and secure algorithm is the already-mentioned Localization with a Mobile Beacon (MBL) [23] Although MBL is not designed to be a secure algorithm and the authors do not mention anything about security in their work, the algorithm used in the MBL is quite simple, where a mobile beacon walks the network and broadcasts its position information to near nodes In this case, regular nodes only need to listen to the beacon node and they never exchange messages among themselves By being a simple single-hop algorithm, this localization system is secured against a number of distributed attacks such as the wormhole attack 184.

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6 Comparison of Current Solutions A widely known fact in network security is that there is no system that is totally safe and secure There will always be weak points and the question is simply whether they are acceptable In WSNs, this issue becomes a little more complicated due to resource limitations In this case, we need to decide on the required level of security, which is application-dependent, and how many resources can be spent in providing these levels of security Depending on this cost bene t analysis, we can decide which solution or what security techniques will be used to secure the WSN In Table 181, we compare each of the studied proposals, showing which type of security they use as well as some observations about them and their potential weaknesses.

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66 CONCLUDING REMARKS This chapter studied advanced aspects of task scheduling The rst sections presented scheduling techniques that are utilized in scheduling algorithms to improve the quality of the produced schedules The insertion technique tries to use idle slots between already scheduled nodes to improve the processor utilization ef ciency Node duplication, on the other hand, aims at reducing the communication costs of schedules Following the framework approach of this text, they were analyzed detached from concrete algorithms in a general manner Up to that point, everything was based on the scheduling model as introduced in Section 41 In Section 63 it was extended toward heterogeneous processors The extension is simple but powerful nd most of the discussed scheduling techniques can be employed with little or no modi cation After this, the chapter returned to a topic that had been more or less skipped in 4 the analysis of special cases of the scheduling problem Unfortunately, most of them remain NP-hard, as could be seen in the extensive survey of scheduling problems in Section 64 The survey employed the | | notation for the classi cation of the various scheduling problems As the scheduling problem is in general NP-hard, genetic algorithms have been employed for its solution This chapter discussed how genetic algorithms can be applied to task scheduling and the implications of different approaches Genetic algorithms are employed in many other areas apart from task scheduling But the GA approach is not the only generic technique that has been applied to task scheduling Integer linear programming (ILP) is a technique that is utilized for all sorts of scheduling problems Hanen and Munier [85] formulate an integer linear programming (ILP) based algorithm for task scheduling Another universal technique, which has been applied recently to task scheduling (Kwok and Ahmad [115]) is A* search (Berman and Paul [22]) In contrast to stochastic search algorithms,A* explores the entire search space systematically and therefore obtains the optimal result It tries to reduce the runtime by pruning of unpromising state subtrees For task scheduling, its runtime is nevertheless exponential; therefore, it can only be applied to relatively small graphs It goes without saying that there are many other interesting aspects of scheduling One last example shall be scheduling tools Many task scheduling algorithms have been implemented in graph based parallelization tools In the parallelization process of such tools, a graph is generated from an initial program speci cation This.

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